Compare commits

..
Author SHA1 Message Date
admin 72ba7949f6 it's too much 2025-07-31 22:24:41 -07:00
admin 8d37c07f24 two still failing 2025-07-31 22:12:25 -07:00
admin 44b3467306 a little cleanup 2025-07-31 21:35:01 -07:00
admin 5d66603e1b another test 2025-07-31 21:29:24 -07:00
admin 96798a6ad5 another test 2025-07-31 21:29:24 -07:00
admin 9273fb0134 one more passing 2025-07-31 21:29:23 -07:00
admin a874e973e2 progress 2025-07-31 21:29:23 -07:00
admin 46a88d17c1 more tests 2025-07-31 21:29:23 -07:00
admin c391ce0a4b handle province stats in client and fix some tests 2025-07-31 21:29:23 -07:00
1357 changed files with 41819 additions and 83920 deletions
+2 -8
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@@ -1,8 +1,5 @@
bazel-1.0.0.bazelrc
# for now: filter out annoying TASTY warnings
common --ui_event_filters=-INFO
common --enable_bzlmod
# Don't use toolchains_llvm for the swift app build
@@ -19,9 +16,9 @@ common --worker_sandboxing
common --local_test_jobs=64
common --jobs=64
common --cxxopt="--std=c++23"
common --cxxopt="--std=c++20"
common --cxxopt="-Wno-deprecated-non-prototype"
common --host_cxxopt="--std=c++23"
common --host_cxxopt="--std=c++20"
common --javacopt="-Xlint:-options"
@@ -29,9 +26,6 @@ common --javacopt="-Xlint:-options"
common --linkopt=-Wl
common:macos --linkopt=-Wl,-no_warn_duplicate_libraries
# Fix Xcode version caching issue - avoids need for `bazel clean --expunge` after Xcode updates
common:macos --repo_env=DEVELOPER_DIR=/Applications/Xcode.app/Contents/Developer
common --java_language_version=17
common --java_runtime_version=remotejdk_17
common --tool_java_language_version=17
-3
View File
@@ -1,3 +0,0 @@
CompileFlags:
Add:
- "-std=c++23"
-3
View File
@@ -6,7 +6,4 @@
*.bytes filter=lfs diff=lfs merge=lfs -text
*.psd filter=lfs diff=lfs merge=lfs -text
*.ttf filter=lfs diff=lfs merge=lfs -text
# Exclude pre-existing font files that were committed as blobs (not LFS pointers)
src/main/csharp/**/GUI[[:space:]]Pro[[:space:]]Kit*/**/*.ttf !filter !diff !merge
src/main/csharp/**/Modern[[:space:]]UI[[:space:]]Pack/**/*.ttf !filter !diff !merge
*.herodata filter=lfs diff=lfs merge=lfs -text
+1 -45
View File
@@ -34,54 +34,10 @@ jobs:
with:
lfs: false
- name: Run tests
id: test
continue-on-error: true
run: bazel test --build_event_json_file=test.json //src/test/... //src/main/go/...
- name: Collect failed test logs
if: always()
run: |
# Remove any existing failed_test_logs directory and create fresh
rm -rf failed_test_logs
mkdir -p failed_test_logs
# Extract failed test targets from test.json and copy their logs
# The test.json is in JSONL format - one JSON object per line
# We look for lines with testResult that have a status other than PASSED
if [ -f test.json ]; then
grep '"testResult"' test.json | \
grep '"status"' | \
grep -v '"status":"PASSED"' | \
grep -o '"label":"[^"]*"' | \
cut -d'"' -f4 | \
sort -u | \
while read target; do
# Convert target like //src/test/cpp/...:test_name to path
log_path=$(echo "$target" | sed 's|^//||' | sed 's|:|/|')
if [ -f "bazel-testlogs/$log_path/test.log" ]; then
log_name=$(echo "$log_path" | tr '/' '_')
if cp "bazel-testlogs/$log_path/test.log" "failed_test_logs/${log_name}.log"; then
echo "Collected log for failed test: $target"
else
echo "Error: Failed to copy log for $target"
fi
fi
done
fi
# List what we collected
echo "Collected logs:"
ls -lh failed_test_logs/ 2>/dev/null || echo "No logs collected"
- name: Archive test results
if: always()
if: success() || failure()
uses: actions/upload-artifact@v4
with:
name: test.json
path: test.json
- name: Archive failed test logs
if: always()
uses: actions/upload-artifact@v4
with:
name: failed-test-logs
path: failed_test_logs/
if-no-files-found: ignore
- name: Fail if tests failed
if: steps.test.outcome == 'failure'
run: exit 1
-63
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@@ -1,63 +0,0 @@
name: Build Linux Sysroot
on:
workflow_dispatch:
inputs:
version:
description: 'Sysroot version (e.g., v2, v3)'
required: true
default: 'v2'
type: string
permissions:
contents: read
jobs:
build-sysroot:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Build sysroot
run: ./tools/sysroot/build_sysroot.sh
- name: Upload sysroot artifact
uses: actions/upload-artifact@v4
with:
name: ubuntu-noble-sysroot
path: tools/sysroot/output/
- name: Install AWS CLI
run: |
sudo apt-get update
sudo apt-get install -y awscli
- name: Upload to DigitalOcean Spaces
env:
AWS_ACCESS_KEY_ID: ${{ secrets.ACCESS_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.SECRET_KEY }}
run: |
# Upload sysroot tarball to DO Spaces
aws s3 cp tools/sysroot/output/ubuntu_noble_amd64_sysroot.tar.xz \
s3://eagle0/sysroot/${{ inputs.version }}/ubuntu_noble_amd64_sysroot.tar.xz \
--endpoint-url https://sfo3.digitaloceanspaces.com \
--acl public-read
# Upload sha256 file
aws s3 cp tools/sysroot/output/ubuntu_noble_amd64_sysroot.sha256 \
s3://eagle0/sysroot/${{ inputs.version }}/ubuntu_noble_amd64_sysroot.sha256 \
--endpoint-url https://sfo3.digitaloceanspaces.com \
--acl public-read
echo ""
echo "=== Sysroot uploaded ==="
echo "URL: https://eagle0.sfo3.digitaloceanspaces.com/sysroot/${{ inputs.version }}/ubuntu_noble_amd64_sysroot.tar.xz"
echo "SHA256: $(cat tools/sysroot/output/ubuntu_noble_amd64_sysroot.sha256)"
echo ""
echo "Update MODULE.bazel with:"
echo "sysroot("
echo " name = \"linux_sysroot\","
echo " sha256 = \"$(cat tools/sysroot/output/ubuntu_noble_amd64_sysroot.sha256)\","
echo " urls = [\"https://eagle0.sfo3.digitaloceanspaces.com/sysroot/${{ inputs.version }}/ubuntu_noble_amd64_sysroot.tar.xz\"],"
echo ")"
-72
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@@ -1,72 +0,0 @@
name: Docker Build and Push
on:
push:
branches: [ "main" ]
paths:
- 'src/main/cpp/**'
- 'src/main/scala/**'
- 'src/main/protobuf/**'
- 'src/main/resources/**'
- 'ci/BUILD.bazel'
- 'MODULE.bazel'
- '.github/workflows/docker_build.yml'
workflow_dispatch:
inputs:
push_images:
description: 'Push images to container registry'
required: true
default: 'false'
type: boolean
permissions:
contents: read
jobs:
build-eagle:
runs-on: self-hosted
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
lfs: false
- name: Build Eagle Docker image
run: bazel build //ci:eagle_server_image
- name: Login to DigitalOcean Container Registry
if: github.event_name == 'push' || (github.event_name == 'workflow_dispatch' && github.event.inputs.push_images == 'true')
env:
DO_TOKEN: ${{ secrets.DO_REGISTRY_TOKEN }}
run: |
mkdir -p ~/.docker
AUTH=$(echo -n "${DO_TOKEN}:${DO_TOKEN}" | base64)
echo "{\"auths\":{\"registry.digitalocean.com\":{\"auth\":\"${AUTH}\"}}}" > ~/.docker/config.json
- name: Push Eagle image to DO registry
if: github.event_name == 'push' || (github.event_name == 'workflow_dispatch' && github.event.inputs.push_images == 'true')
run: bazel run //ci:eagle_server_push
build-shardok:
runs-on: self-hosted
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
lfs: false
- name: Build Shardok Docker image (cross-compile for Linux)
run: bazel build --platforms=//:linux_x86_64 //ci:shardok_server_image
- name: Login to DigitalOcean Container Registry
if: github.event_name == 'push' || (github.event_name == 'workflow_dispatch' && github.event.inputs.push_images == 'true')
env:
DO_TOKEN: ${{ secrets.DO_REGISTRY_TOKEN }}
run: |
mkdir -p ~/.docker
AUTH=$(echo -n "${DO_TOKEN}:${DO_TOKEN}" | base64)
echo "{\"auths\":{\"registry.digitalocean.com\":{\"auth\":\"${AUTH}\"}}}" > ~/.docker/config.json
- name: Push Shardok image to DO registry
if: github.event_name == 'push' || (github.event_name == 'workflow_dispatch' && github.event.inputs.push_images == 'true')
run: bazel run --platforms=//:linux_x86_64 //ci:shardok_server_push
+2 -2
View File
@@ -20,7 +20,7 @@ project/boot/
project/plugins/project/
project/target/
bazel-bin
bazel-eagle0*
bazel-eagle0
bazel-out
bazel-testlogs
.ijwb
@@ -32,9 +32,9 @@ buildWin.sh
__pycache__/
scripts/refresh_name_layers/vendor/
scripts/refresh_name_layers/refresh_name_layers.zip
.pre-commit-config.yaml
.bazelbsp
.bsp
.metals
api_keys.txt
src/main/csharp/net/eagle0/clients/unity/eagle0/ProjectSettings/Packages/com.unity.dedicated-server/
-44
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@@ -1,44 +0,0 @@
# See https://pre-commit.com for more information
# See https://pre-commit.com/hooks.html for more hooks
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.3.0
hooks:
- id: check-added-large-files
- id: no-commit-to-branch
args: [--branch, main]
- repo: https://github.com/pocc/pre-commit-hooks
rev: v1.3.5
hooks:
- id: clang-format
args: [-i, --no-diff]
types_or: ["c++", "c#"]
exclude: ^src/main/csharp/net/eagle0/clients/unity/eagle0/Assets/Plugins
- repo: https://github.com/yoheimuta/protolint
rev: v0.42.2
hooks:
- id: protolint
args: [-fix]
exclude: ^src/main/protobuf/scalapb/
- repo: local
hooks:
- id: scalafmt
name: scalafmt
language: system
entry: scalafmt -i -f
types_or: ["scala"]
- repo: local
hooks:
- id: gazelle
name: gazelle
language: system
entry: ./scripts/pre-commit-gazelle.sh
files: '(\.go|\.proto|BUILD\.bazel|BUILD|WORKSPACE|WORKSPACE\.bazel|\.bzl)$'
pass_filenames: false
- repo: local
hooks:
- id: update-action-result-types
name: update-action-result-types
language: system
entry: ./scripts/updateActionResultTypes.sh
files: 'src/main/protobuf/net/eagle0/eagle/common/action_result_type.proto'
+2 -47
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@@ -1,47 +1,2 @@
version = "3.9.9"
runner.dialect = scala3
rewrite.scala3.convertToNewSyntax = true
# Keep braces, don't use significant indentation
# rewrite.scala3.removeOptionalBraces = yes
rewrite.scala3.insertEndMarkerMinLines = 15
rewrite.scala3.removeEndMarkerMaxLines = 14
# Strip margin settings
assumeStandardLibraryStripMargin = false
align.stripMargin = true
# Code Style & Formatting
align.preset = more
align.multiline = true
align.arrowEnumeratorGenerator = true
spaces.inImportCurlyBraces = false
spaces.beforeContextBoundColon = Never
maxColumn = 120
docstrings.style = Asterisk
docstrings.wrap = yes
# Method chaining
newlines.beforeCurlyLambdaParams = multilineWithCaseOnly
optIn.breakChainOnFirstMethodDot = true
includeCurlyBraceInSelectChains = false
# Advanced Scala 3 Features
rewrite.scala3.countEndMarkerLines = all
rewrite.redundantBraces.stringInterpolation = true
rewrite.redundantBraces.parensForOneLineApply = true
# Project-Specific Considerations
optIn.annotationNewlines = true
runner.optimizer.forceConfigStyleMinArgCount = 3
# Import sorting configuration
rewrite.rules = [SortImports, RedundantBraces, RedundantParens]
rewrite.imports.sort = scalastyle
rewrite.imports.groups = [
["java\\..*"],
["javax\\..*"],
["scala\\..*"],
[".*"]
]
rewrite.imports.contiguousGroups = only
rewrite.trailingCommas.style = never
version = "3.6.1"
runner.dialect = scala213
-9
View File
@@ -3,15 +3,6 @@ load("@io_bazel_rules_go//go:def.bzl", "nogo")
package(default_visibility = ["//visibility:public"])
# Platform for cross-compiling to Linux x86_64
platform(
name = "linux_x86_64",
constraint_values = [
"@platforms//os:linux",
"@platforms//cpu:x86_64",
],
)
gazelle(name = "gazelle")
# gazelle:proto file
+5 -154
View File
@@ -4,32 +4,26 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
## Project Overview
Eagle0 is a multi-language gaming system combining strategic turn-based gameplay (Eagle) with tactical hex-based
combat (Shardok). The system integrates LLM-based narrative generation and supports both human and AI players.
Eagle0 is a multi-language gaming system combining strategic turn-based gameplay (Eagle) with tactical hex-based combat (Shardok). The system integrates LLM-based narrative generation and supports both human and AI players.
## Architecture
**Three-Tier Game System:**
- **Unity Client (C#)**: Real-time strategy game client with integrated tactical combat UI
- **Eagle (Scala)**: Strategic layer managing turn-based gameplay, diplomacy, hero progression, and province control
- **Shardok (C++)**: Tactical layer handling real-time hex-based combat simulation with performance-critical battle
resolution
- **Shardok (C++)**: Tactical layer handling real-time hex-based combat simulation with performance-critical battle resolution
**Communication Flow:**
```
Unity Client ↔ Eagle (gRPC streaming) ↔ Shardok (internal gRPC)
```
**Key Entry Points:**
- `/src/main/csharp/net/eagle0/clients/unity/eagle0/` - Unity C# game client
- `/src/main/scala/net/eagle0/eagle/Main.scala` - Eagle strategic game server
- `/src/main/cpp/net/eagle0/shardok/shardok_server_main.cpp` - Shardok tactical server
**Protocol Buffer Architecture:**
- Extensive use of protobuf for type-safe communication
- Separate packages: `api/` (client-facing), `internal/` (server state), `views/` (client projections)
- Event sourcing pattern with immutable action history
@@ -37,17 +31,13 @@ Unity Client ↔ Eagle (gRPC streaming) ↔ Shardok (internal gRPC)
## Essential Commands
### Building
```bash
# Build Eagle server (Scala strategic layer)
bazel build //src/main/scala/net/eagle0/eagle:eagle_server_deploy.jar
# Build Shardok server (C++ tactical layer)
# Build Shardok server (C++ tactical layer)
bazel build -c opt //src/main/cpp/net/eagle0/shardok:shardok-server
# Shardok server includes both AI algorithms
bazel build //src/main/cpp/net/eagle0/shardok:shardok-server
# Build Unity/C# client
./scripts/build_protos.sh # Protocol buffer generation for Unity
./scripts/build_plugins.sh # Native plugins for all platforms
@@ -56,7 +46,6 @@ bazel build //src/main/cpp/net/eagle0/shardok:shardok-server
```
### Running Services
```bash
# Eagle server (port 40032)
bazel run //src/main/scala/net/eagle0/eagle:eagle_server -- --eagle-grpc-port 40032
@@ -68,7 +57,6 @@ bazel run //src/main/cpp/net/eagle0/shardok:shardok-server --compilation_mode=op
```
### Testing
```bash
# Run all tests
bazel test //src/test/... //src/main/go/...
@@ -79,24 +67,12 @@ bazel test //src/test/cpp/... # C++ Shardok tests
```
### Code Generation
```bash
bazel run gazelle # Update Go build files
./scripts/updateActionResultTypes.sh # Update protocol buffer mappings
```
### Pre-Commit Checklist
**MANDATORY: Before running `git commit`, verify:**
1. **If you modified any BUILD.bazel file:** Run `bazel run gazelle` and stage any changes it makes
2. **If you modified C++ or C# files:** Run `clang-format -i` on the modified files
3. **If you modified Scala files:** scalafmt will run automatically via pre-commit hook
The pre-commit hook runs gazelle but only checks if it succeeds - it does NOT verify the BUILD files are in canonical format. The `gazelle_test` will fail if deps are not alphabetically sorted. **Always run gazelle manually after BUILD file changes.**
### Code Formatting
```bash
# ALWAYS run clang-format after making any C++ or C# code changes
clang-format -i <modified_files>
@@ -108,95 +84,26 @@ find . -name "*.cpp" -o -name "*.hpp" | xargs clang-format -i
find . -name "*.cs" | xargs clang-format -i
```
### Static Analysis
```bash
# Run clang-tidy static analysis on C++ files
# Note: This may show some header include errors but will still analyze the main file
bazel run @llvm_toolchain//:clang-tidy -- --checks='readability-*,bugprone-*,clang-analyzer-*' <file_path> -- -I/Users/dancrosby/CodingProjects/github/eagle0 -std=c++23
# Example for AI files:
bazel run @llvm_toolchain//:clang-tidy -- --checks='readability-*,bugprone-*,clang-analyzer-*' /Users/dancrosby/CodingProjects/github/eagle0/src/main/cpp/net/eagle0/shardok/ai/AIUnitScoreCalculator.cpp -- -I/Users/dancrosby/CodingProjects/github/eagle0 -std=c++23
```
## AI Algorithm Selection
Eagle0 supports two AI algorithms for tactical combat decision-making:
### Iterative Deepening AI (Default)
The original minimax-based AI with sophisticated randomness handling:
- **Advantages**: Proven, sophisticated randomness evaluation, comprehensive lookahead
- **Use cases**: Production builds, scenarios requiring precise evaluation
- **Performance**: Single-threaded, thorough evaluation
### Monte Carlo Tree Search AI (MCTS)
Modern MCTS-based AI with multithreading support:
- **Advantages**: Multithreaded, better performance on modern CPUs, anytime algorithm
- **Use cases**: Performance testing, scenarios requiring fast decisions
- **Performance**: Multithreaded, adaptive depth based on time budget
### Switching Between Algorithms
The algorithm is selected at **runtime** via the ShardokAIClient constructor:
```cpp
// Using Iterative Deepening AI (default)
ShardokAIClient client(playerId, isDefender, hexMap, settings);
// OR explicitly:
ShardokAIClient client(playerId, isDefender, hexMap, settings, AIAlgorithmType::ITERATIVE_DEEPENING);
// Using MCTS AI
ShardokAIClient client(playerId, isDefender, hexMap, settings, AIAlgorithmType::MCTS);
```
```bash
# Build the server (includes both AI algorithms)
bazel build //src/main/cpp/net/eagle0/shardok:shardok-server
# Test both algorithms
bazel test //src/test/cpp/net/eagle0/shardok/ai:ai_iterative_deepening_test
bazel test //src/test/cpp/net/eagle0/shardok/ai:ai_mcts_test # If available
# Performance tests
./scripts/ai_perf_test.sh # Uses whatever algorithm the server is configured to use
```
Both implementations are compatible with all existing interfaces and produce the same `SearchResult` structure.
**Note**: Both implementations are documented in `src/main/cpp/net/eagle0/shardok/ai/AI_SCORING_SYSTEM.md`, including
recommendations for improving MCTS randomness handling.
The AI algorithm selection is made at runtime when creating ShardokAIClient instances, allowing different AI strategies
to be used for different players or game situations within the same server process.
## Language-Specific Patterns
**Scala (Strategic Layer):**
- Use `EngineImpl.scala` for core game logic modifications
- Follow event sourcing pattern - all changes through immutable actions
- gRPC streaming for real-time client updates via `EagleServiceImpl.scala`
- LLM integration in `/common/llm_integration/` for narrative generation
**C++ (Tactical Layer):**
- Performance-critical combat in `ShardokEngine.hpp/.cpp`
- FlatBuffers for efficient serialization in `/flatbuffer/` directory
- AI systems in `/ai/` subdirectory with pluggable strategy selectors
- Extensive unit testing with Google Test framework
**Protocol Buffers:**
- Three-layer structure: `api/` (client), `internal/` (server), `views/` (projections)
- Use `shardok_internal_interface.proto` for Eagle-Shardok communication
- Maintain backward compatibility when modifying existing messages
**C# (Unity Client):**
- Located in `/src/main/csharp/net/eagle0/clients/unity/eagle0/`
- Uses Unity 6 (6000.0.32f1) with comprehensive protobuf integration (100+ .proto files)
- Key components: `EagleConnection.cs` (gRPC client), `EagleGameController.cs` (main game logic)
@@ -205,7 +112,6 @@ to be used for different players or game situations within the same server proce
- Seamless transition between strategic gameplay and hex-based tactical combat
**Go (Build Tools):**
- Build automation and code generation utilities
- AWS S3 integration for deployment artifacts
@@ -216,31 +122,6 @@ to be used for different players or game situations within the same server proce
- Map validation tests ensure game content integrity
- Use `GameSettings_test_utils.cpp` and `ShardokEngineBasedTestData.cpp` for C++ test helpers
### Scala Testing Patterns
**Use `inside()` instead of `asInstanceOf` for type matching in tests:**
Never use `asInstanceOf` in tests. Instead, use ScalaTest's `inside()` pattern for safe type matching:
```scala
// BAD - don't do this
val changedHero = result.changedHeroes.head.asInstanceOf[ChangedHeroC]
changedHero.heroId shouldBe 19
// GOOD - use inside() pattern
import org.scalatest.Inside.inside
inside(result.changedHeroes.head) { case changedHero: ChangedHeroC =>
changedHero.heroId shouldBe 19
changedHero.vigorChange shouldBe StatDelta(17.2)
}
```
The `inside()` pattern:
- Provides better error messages when the type doesn't match
- Is idiomatic ScalaTest
- Works with pattern matching for more complex assertions
## Performance Testing
When making performance-related changes to the AI or engine:
@@ -272,38 +153,10 @@ done
```
**Important notes:**
- Run tests multiple times (3-5) to account for performance variance
- Focus on commands evaluated at each depth rather than total commands
- Commands at different depths aren't directly comparable (depth 3 is more valuable than depth 2)
- **Always test performance changes** - what seems like an optimization may sometimes have unexpected overhead or
behavior changes.
## Troubleshooting Scala Build Errors
### MissingType Errors
When you see errors like:
```
dotty.tools.dotc.core.MissingType: Cannot resolve reference to type net.eagle0.eagle.internal.game_state.type.GameState
```
**This is NOT a Scala compiler crash.** This is a missing dependency in BUILD.bazel.
**How to fix:**
1. Identify the missing type from the error message (e.g., `game_state.GameState`)
2. Find the Bazel target that provides this type (e.g., `//src/main/protobuf/net/eagle0/eagle/internal:game_state_scala_proto`)
3. Add it to the `deps` of the failing target
4. If the type appears in a public method signature, also add it to `exports` so downstream targets can see it
**Common pattern:** When adding a method to a class that takes or returns a proto type, the proto dependency often needs to be added to both `deps` AND `exports`.
### Bazel Clean
**NEVER run `bazel clean` without asking first.** It rarely fixes actual issues and wastes significant rebuild time. The issues that seem like they need `bazel clean` are usually:
- Missing imports in Scala code
- Missing dependencies in BUILD.bazel
- Missing exports for types used in public signatures
- **Always test performance changes** - what seems like an optimization may sometimes have unexpected overhead or behavior changes.
## Game Content
@@ -315,6 +168,4 @@ dotty.tools.dotc.core.MissingType: Cannot resolve reference to type net.eagle0.e
- Bazel handles multi-language builds and dependencies
- CI/CD via GitHub Actions with platform-specific build scripts in `/ci/github_actions/`
- Docker containerization available via `ci/eagle_run.Dockerfile`
- Always run "bazel run //:gazelle" after editing any BUILD.bazel files
- *ALWAYS ALWAYS* run "bazel run gazelle" after any change that modifies a BUILD.bazel file
- Docker containerization available via `ci/eagle_run.Dockerfile`
+103 -199
View File
@@ -1,269 +1,173 @@
module(name = "net_eagle0")
# Version constants
SCALA_VERSION = "3.7.2"
NETTY_VERSION = "4.1.110.Final"
SCALAPB_VERSION = "1.0.0-alpha.1"
AWS_SDK_VERSION = "2.28.1"
bazel_dep(name = "apple_support", repo_name = "build_bazel_apple_support", version = "1.21.1")
#
# Core Build Tools
# bazel-toolchain
#
bazel_dep(name = "bazel_skylib", version = "1.8.1")
bazel_dep(name = "rules_pkg", version = "1.1.0")
#
# Language Support - Scala
#
bazel_dep(name = "rules_scala", version = "7.1.1")
scala_config = use_extension(
"@rules_scala//scala/extensions:config.bzl",
"scala_config",
)
scala_config.settings(scala_version = SCALA_VERSION)
scala_deps = use_extension(
"@rules_scala//scala/extensions:deps.bzl",
"scala_deps",
)
scala_deps.scala()
scala_deps.scalatest()
scala_deps.scala_proto()
#
# Language Support - C++
#
bazel_dep(name = "toolchains_llvm", version = "1.6.0")
bazel_dep(name = "toolchains_llvm", version = "1.2.0")
# Configure and register the toolchain.
llvm = use_extension("@toolchains_llvm//toolchain/extensions:llvm.bzl", "llvm")
# Native toolchain (macOS -> macOS, Linux -> Linux)
llvm.toolchain(
name = "llvm_toolchain",
llvm_version = "20.1.2",
llvm_version = "19.1.0",
)
# Cross-compilation toolchain (macOS -> Linux x86_64)
# Uses the same LLVM distribution but with a Linux sysroot
llvm.toolchain(
name = "llvm_toolchain_linux",
llvm_version = "20.1.2",
use_repo(llvm, "llvm_toolchain")
# Set dev_dependency so we can turn this off for swift MacOS builds
register_toolchains(
"@llvm_toolchain//:all",
dev_dependency = True,
)
# Linux sysroot for cross-compilation (Chromium's Debian sysroot)
llvm.sysroot(
name = "llvm_toolchain_linux",
label = "@linux_sysroot//sysroot",
targets = ["linux-x86_64"],
)
use_repo(llvm, "llvm_toolchain", "llvm_toolchain_linux")
# Download the Linux sysroot (Ubuntu 24.04 Noble for C++23 support)
# Built by: .github/workflows/build_sysroot.yml
# To rebuild: Run the "Build Linux Sysroot" workflow with a new version, then update sha256 and URL
sysroot = use_repo_rule("@toolchains_llvm//toolchain:sysroot.bzl", "sysroot")
sysroot(
name = "linux_sysroot",
# TODO: Update sha256 after running sysroot build workflow with version v2
sha256 = "PLACEHOLDER_RUN_SYSROOT_WORKFLOW_FIRST",
urls = ["https://eagle0.sfo3.digitaloceanspaces.com/sysroot/v2/ubuntu_noble_amd64_sysroot.tar.xz"],
)
#
# Language Support - Go
#
bazel_dep(name = "rules_go", version = "0.56.1", repo_name = "io_bazel_rules_go")
bazel_dep(name = "gazelle", version = "0.45.0", repo_name = "bazel_gazelle")
bazel_dep(name = "rules_pkg", version = "1.0.1")
bazel_dep(name = "bazel_skylib", version = "1.7.1")
bazel_dep(name = "protobuf", repo_name = "com_google_protobuf", version = "29.2")
bazel_dep(name = "grpc", version = "1.71.0")
bazel_dep(name = "grpc-java", version = "1.71.0")
bazel_dep(name = "googletest", version = "1.15.2")
bazel_dep(name = "rules_go", repo_name = "io_bazel_rules_go", version = "0.50.1")
bazel_dep(name = "gazelle", repo_name = "bazel_gazelle", version = "0.40.0")
go_sdk = use_extension("@io_bazel_rules_go//go:extensions.bzl", "go_sdk")
go_sdk.download(version = "1.23.3")
go_deps = use_extension("@bazel_gazelle//:extensions.bzl", "go_deps")
go_deps.from_file(go_mod = "//:go.mod")
use_repo(
go_deps,
"com_github_aws_aws_sdk_go_v2",
"com_github_aws_aws_sdk_go_v2_config",
"com_github_aws_aws_sdk_go_v2_credentials",
"com_github_aws_aws_sdk_go_v2_service_s3",
"org_golang_google_grpc",
"org_golang_google_protobuf",
"org_golang_x_text",
"com_github_google_go_cmp",
)
#
# Platform Support - Apple/iOS
#
bazel_dep(name = "apple_support", version = "1.21.1", repo_name = "build_bazel_apple_support")
bazel_dep(name = "rules_apple", version = "3.16.1", repo_name = "build_bazel_rules_apple")
bazel_dep(name = "rules_swift", version = "2.3.1", repo_name = "build_bazel_rules_swift")
#go_sdk.nogo(
# nogo = "//:my_nogo",
#)
#
# Protocol Buffers & RPC
# rules_jvm_external
#
bazel_dep(name = "protobuf", version = "29.2", repo_name = "com_google_protobuf")
bazel_dep(name = "grpc", version = "1.71.0")
bazel_dep(name = "grpc-java", version = "1.71.0")
bazel_dep(name = "flatbuffers", version = "25.2.10")
scala_version = "2.13.14"
#
# Testing
#
bazel_dep(name = "googletest", version = "1.17.0")
#
# Container Images (OCI)
#
bazel_dep(name = "rules_oci", version = "2.2.6")
bazel_dep(name = "aspect_bazel_lib", version = "2.16.0")
oci = use_extension("@rules_oci//oci:extensions.bzl", "oci")
# Base image for Eagle (Java 17)
oci.pull(
name = "eclipse_temurin_17",
digest = "sha256:d286b5352d98777bbf727f54038b04f0145cd9b76ca83f38a67aa111d4303748",
image = "docker.io/library/eclipse-temurin",
platforms = ["linux/amd64"],
bazel_dep(
name = "rules_jvm_external",
version = "6.3",
)
# Base image for Shardok (Ubuntu 24.04 for C++ runtime)
oci.pull(
name = "ubuntu_24_04",
image = "docker.io/library/ubuntu",
platforms = ["linux/amd64"],
tag = "24.04",
)
use_repo(oci, "eclipse_temurin_17", "eclipse_temurin_17_linux_amd64", "ubuntu_24_04", "ubuntu_24_04_linux_amd64")
#
# Java/Scala Dependencies
#
bazel_dep(name = "rules_jvm_external", version = "6.3")
maven = use_extension("@rules_jvm_external//:extensions.bzl", "maven")
maven.install(
artifacts = [
# Netty
"io.netty:netty-codec:%s" % NETTY_VERSION,
"io.netty:netty-codec-http:%s" % NETTY_VERSION,
"io.netty:netty-codec-socks:%s" % NETTY_VERSION,
"io.netty:netty-codec-http2:%s" % NETTY_VERSION,
"io.netty:netty-handler:%s" % NETTY_VERSION,
"io.netty:netty-buffer:%s" % NETTY_VERSION,
"io.netty:netty-transport:%s" % NETTY_VERSION,
"io.netty:netty-resolver:%s" % NETTY_VERSION,
"io.netty:netty-common:%s" % NETTY_VERSION,
"io.netty:netty-handler-proxy:%s" % NETTY_VERSION,
# ScalaPB
"com.thesamet.scalapb:lenses_3:%s" % SCALAPB_VERSION,
"com.thesamet.scalapb:scalapb-json4s_3:%s" % SCALAPB_VERSION,
"com.thesamet.scalapb:scalapb-runtime_3:%s" % SCALAPB_VERSION,
"com.thesamet.scalapb:scalapb-runtime-grpc_3:%s" % SCALAPB_VERSION,
"com.thesamet.scalapb:compilerplugin_3:%s" % SCALAPB_VERSION,
"com.thesamet.scalapb:protoc-bridge_3:0.9.9",
# JSON
"org.json4s:json4s-ast_3:4.1.0-M8",
"org.json4s:json4s-core_3:4.1.0-M8",
"org.json4s:json4s-native_3:4.1.0-M8",
# Testing
"org.scalamock:scalamock_3:7.4.1",
# AWS SDK
"software.amazon.awssdk:s3-transfer-manager:%s" % AWS_SDK_VERSION,
"software.amazon.awssdk:s3:%s" % AWS_SDK_VERSION,
"software.amazon.awssdk:regions:%s" % AWS_SDK_VERSION,
"software.amazon.awssdk:aws-core:%s" % AWS_SDK_VERSION,
"software.amazon.awssdk:sdk-core:%s" % AWS_SDK_VERSION,
"software.amazon.awssdk:utils:%s" % AWS_SDK_VERSION,
"software.amazon.awssdk:http-client-spi:%s" % AWS_SDK_VERSION,
# AWS Lambda
"com.amazonaws:aws-lambda-java-core:1.2.3",
"com.amazonaws:aws-lambda-java-events:3.13.0",
# Logging
"org.scala-lang:scala-library:%s" % scala_version,
"io.netty:netty-codec:4.1.110.Final",
"io.netty:netty-codec-http:4.1.110.Final",
"io.netty:netty-codec-socks:4.1.110.Final",
"io.netty:netty-codec-http2:4.1.110.Final",
"io.netty:netty-handler:4.1.110.Final",
"io.netty:netty-buffer:4.1.110.Final",
"io.netty:netty-transport:4.1.110.Final",
"io.netty:netty-resolver:4.1.110.Final",
"io.netty:netty-common:4.1.110.Final",
"io.netty:netty-handler-proxy:4.1.110.Final",
"com.thesamet.scalapb:lenses_2.13:1.0.0-alpha.1",
"com.thesamet.scalapb:scalapb-json4s_2.13:1.0.0-alpha.1",
"com.thesamet.scalapb:scalapb-runtime_2.13:1.0.0-alpha.1",
"com.thesamet.scalapb:scalapb-runtime-grpc_2.13:1.0.0-alpha.1",
"com.thesamet.scalapb:compilerplugin_2.13:1.0.0-alpha.1",
"com.thesamet.scalapb:protoc-bridge_2.13:0.9.8",
"org.json4s:json4s-ast_2.13:4.0.7",
"org.json4s:json4s-core_2.13:4.0.7",
"org.json4s:json4s-native_2.13:4.0.7",
"org.scalamock:scalamock_2.13:6.0.0",
"software.amazon.awssdk:s3-transfer-manager:2.28.1",
"software.amazon.awssdk:s3:2.28.1",
"software.amazon.awssdk:regions:2.28.1",
"software.amazon.awssdk:aws-core:2.28.1",
"software.amazon.awssdk:sdk-core:2.28.1",
"org.slf4j:slf4j-api:2.0.16",
"org.slf4j:slf4j-simple:2.0.16",
# Other
#"software.amazon.awssdk:sns:2.28.1",
"software.amazon.awssdk:utils:2.28.1",
"software.amazon.awssdk:http-client-spi:2.28.1",
"org.reactivestreams:reactive-streams:1.0.4",
"com.amazonaws:aws-lambda-java-core:1.2.3",
"com.amazonaws:aws-lambda-java-events:3.13.0",
"javax.xml.bind:jaxb-api:2.3.1",
# OkHttp (for SSE with read timeout support)
"com.squareup.okhttp3:okhttp:4.12.0",
"com.squareup.okhttp3:okhttp-sse:4.12.0",
],
duplicate_version_warning = "error",
fail_if_repin_required = True,
lock_file = "//:maven_install.json",
lock_file = "//:maven_install.json", #
repositories = [
"https://repo1.maven.org/maven2",
],
)
use_repo(maven, "maven", "unpinned_maven")
#
# External Libraries
# rules_apple
#
bazel_dep(
name = "rules_apple",
repo_name = "build_bazel_rules_apple",
version = "3.16.1",
)
bazel_dep(
name = "rules_swift",
repo_name = "build_bazel_rules_swift",
version = "2.3.1",
)
#
# Unbazelified imports
#
http_archive = use_repo_rule("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
# GTL (for parallel_hashmap)
GTL_VERSION = "1.2.0"
#
# flatbuffers
#
bazel_dep(name = "flatbuffers", version = "25.2.10")
GTL_SHA = "1969c45dd76eac0dd87e9e2b65cffe358617f4fe1bcd203f72f427742537913a"
#
# gtl (for parallel_hashmap)
#
gtl_version = "1.2.0"
gtl_sha = "1969c45dd76eac0dd87e9e2b65cffe358617f4fe1bcd203f72f427742537913a"
http_archive(
name = "gtl",
build_file = "@//external:BUILD.gtl",
sha256 = GTL_SHA,
strip_prefix = "gtl-%s" % GTL_VERSION,
url = "https://github.com/greg7mdp/gtl/archive/refs/tags/v%s.zip" % GTL_VERSION,
sha256 = gtl_sha,
strip_prefix = "gtl-%s" % gtl_version,
url = "https://github.com/greg7mdp/gtl/archive/refs/tags/v%s.zip" % gtl_version,
)
# Unity GoDice Plugin
UNITY_GODICE_COMMIT = "18d6823991592e4d45fcc0f22692db849dea9063"
#
# Plugins for the native code for interacting with GoDice
#
unity_godice_commit = "18d6823991592e4d45fcc0f22692db849dea9063"
UNITY_GODICE_SHA = "04e6ae4155965aab3372592e04061eba1256bb6ea7ccffd0d83f27574e5b3349"
unity_godice_sha = "04e6ae4155965aab3372592e04061eba1256bb6ea7ccffd0d83f27574e5b3349"
http_archive(
name = "net_eagle0_unity_godice",
sha256 = UNITY_GODICE_SHA,
strip_prefix = "godice-framework-%s" % UNITY_GODICE_COMMIT,
sha256 = unity_godice_sha,
strip_prefix = "godice-framework-%s" % unity_godice_commit,
urls = [
"https://github.com/nolen777/godice-framework/archive/%s.zip" % UNITY_GODICE_COMMIT,
"https://github.com/nolen777/godice-framework/archive/%s.zip" % unity_godice_commit,
],
)
#
# Toolchain Registration
#
register_toolchains(
"//tools:unused_dependency_checker_error_and_opts_toolchain",
"@rules_scala//testing:scalatest_toolchain",
)
# Set dev_dependency so we can turn this off for swift MacOS builds
register_toolchains(
"@llvm_toolchain//:all",
"@llvm_toolchain_linux//:all",
dev_dependency = True,
)
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+51 -2
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@@ -1,2 +1,51 @@
# This file marks the root of the Bazel workspace.
# See MODULE.bazel for external dependencies and setup.
workspace(name = "net_eagle0")
load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
#
# Scala support
#
scala_version = "2.13.14"
#rules_scala_version = "6.6.0"
#rules_scala_sha = "e734eef95cf26c0171566bdc24d83bd82bdaf8ca7873bec6ce9b0d524bdaf05d"
#http_archive(
# name = "io_bazel_rules_scala",
# sha256 = rules_scala_sha,
# strip_prefix = "rules_scala-%s" % rules_scala_version,
# url = "https://github.com/bazelbuild/rules_scala/releases/download/v%s/rules_scala-v%s.tar.gz" % (rules_scala_version, rules_scala_version),
#)
# Using a commit from master to get 2.13.14 support. Restore the commented-out lines above with a new
# release version when one is cut.
rules_scala_commit = "e53a43bf48f10a5906b3e91c21798281cec1b334"
rules_scala_sha = "b4fd903724d084d9d9f45e17fc22391bda745bf0574f8934d38a9c1c2fc18834"
http_archive(
name = "io_bazel_rules_scala",
sha256 = rules_scala_sha,
strip_prefix = "rules_scala-%s" % rules_scala_commit,
url = "https://github.com/bazelbuild/rules_scala/archive/%s.zip" % rules_scala_commit,
)
load("@io_bazel_rules_scala//:scala_config.bzl", "scala_config")
scala_config(scala_version = scala_version)
load("//tools:toolchains.bzl", "scala_register_toolchains")
scala_register_toolchains()
load("@io_bazel_rules_scala//scala:scala.bzl", "scala_repositories")
scala_repositories()
load("@io_bazel_rules_scala//testing:scalatest.bzl", "scalatest_repositories", "scalatest_toolchain")
scalatest_repositories()
scalatest_toolchain()
-128
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@@ -1,128 +0,0 @@
load("@rules_oci//oci:defs.bzl", "oci_image", "oci_load", "oci_push")
load("@rules_pkg//pkg:tar.bzl", "pkg_tar")
#
# Eagle Server Docker Image
#
# Build: bazel build //ci:eagle_server_image
# Load: bazel run //ci:eagle_server_load
# Push: bazel run //ci:eagle_server_push
#
# Package the deploy JAR
pkg_tar(
name = "eagle_server_jar_layer",
srcs = ["//src/main/scala/net/eagle0/eagle:eagle_server_deploy.jar"],
package_dir = "/app",
)
# Package the game resources needed at runtime
pkg_tar(
name = "eagle_resources_layer",
srcs = [
"//src/main/resources/net/eagle0/eagle:beasts",
"//src/main/resources/net/eagle0/eagle:game_parameters",
"//src/main/resources/net/eagle0/eagle:headshots",
"//src/main/resources/net/eagle0/eagle:heroes",
"//src/main/resources/net/eagle0/eagle:province_map",
"//src/main/resources/net/eagle0/eagle:settings",
],
package_dir = "/app/resources",
)
oci_image(
name = "eagle_server_image",
base = "@eclipse_temurin_17_linux_amd64",
entrypoint = [
"java",
"-Xmx4g",
"-XX:+UseG1GC",
"-jar",
"/app/eagle_server_deploy.jar",
],
env = {
"JAVA_OPTS": "-Xmx4g -XX:+UseG1GC",
},
exposed_ports = ["40032/tcp"],
tars = [
":eagle_server_jar_layer",
":eagle_resources_layer",
],
workdir = "/app",
)
# Load into Docker locally: bazel run //ci:eagle_server_load
oci_load(
name = "eagle_server_load",
image = ":eagle_server_image",
repo_tags = ["eagle0/eagle-server:latest"],
)
# Push to DigitalOcean Container Registry
oci_push(
name = "eagle_server_push",
image = ":eagle_server_image",
repository = "registry.digitalocean.com/eagle0/eagle-server",
)
#
# Shardok Server Docker Image
#
# Build: bazel build //ci:shardok_server_image
# Load: bazel run //ci:shardok_server_load
# Push: bazel run //ci:shardok_server_push
#
# Package the Shardok binary
pkg_tar(
name = "shardok_binary_layer",
srcs = ["//src/main/cpp/net/eagle0/shardok:shardok-server"],
package_dir = "/app",
)
# Package the Shardok resources (battalion types, settings)
pkg_tar(
name = "shardok_resources_layer",
srcs = [
"//src/main/resources/net/eagle0/shardok:battalion_types",
"//src/main/resources/net/eagle0/shardok:settings",
],
package_dir = "/app/resources",
)
# Package the converted maps
pkg_tar(
name = "shardok_maps_layer",
srcs = ["//src/main/resources/net/eagle0/shardok/maps"],
package_dir = "/app/resources/maps",
)
oci_image(
name = "shardok_server_image",
base = "@ubuntu_24_04_linux_amd64",
entrypoint = ["/app/shardok-server"],
exposed_ports = [
"40042/tcp",
"40052/tcp",
],
tars = [
":shardok_binary_layer",
":shardok_resources_layer",
":shardok_maps_layer",
],
workdir = "/app",
)
# Load into Docker locally: bazel run //ci:shardok_server_load
oci_load(
name = "shardok_server_load",
image = ":shardok_server_image",
repo_tags = ["eagle0/shardok-server:latest"],
)
# Push to DigitalOcean Container Registry
oci_push(
name = "shardok_server_push",
image = ":shardok_server_image",
repository = "registry.digitalocean.com/eagle0/shardok-server",
)
+2 -1
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@@ -1 +1,2 @@
UNITY_VERSION='6000.3.0f1'
UNITY_VERSION='6000.1.11f1'
-47
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@@ -1,47 +0,0 @@
# Docker Compose for local testing of production images
# Build images: bazel run //ci:eagle_server_load && bazel run //ci:shardok_server_load
# Run: docker compose -f docker-compose.prod.yml up
services:
eagle:
image: eagle0/eagle-server:latest
container_name: eagle-server
ports:
- "40032:40032"
environment:
# Eagle server configuration
EAGLE_GRPC_PORT: "40032"
SHARDOK_HOST: "shardok"
SHARDOK_PORT: "40042"
# Resource paths (relative to /app in container)
EAGLE_RESOURCES_PATH: "/app/resources"
volumes:
# Mount saves directory for persistence
- ./saves:/app/saves
depends_on:
- shardok
restart: unless-stopped
healthcheck:
test: ["CMD", "nc", "-z", "localhost", "40032"]
interval: 30s
timeout: 10s
retries: 3
start_period: 30s
shardok:
image: eagle0/shardok-server:latest
container_name: shardok-server
ports:
- "40042:40042"
- "40052:40052"
environment:
# Shardok server configuration
SHARDOK_RESOURCES_PATH: "/app/resources"
SHARDOK_MAPS_PATH: "/app/resources/maps"
restart: unless-stopped
healthcheck:
test: ["CMD", "nc", "-z", "localhost", "40042"]
interval: 30s
timeout: 10s
retries: 3
start_period: 10s
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@@ -1,280 +0,0 @@
# CommandProto Usage Analysis in shardok/ai
This document analyzes all remaining usages of `CommandProto` (protocol buffer representation) in the AI code and identifies opportunities to eliminate proto conversion by using `ShardokCommand` directly.
## Summary
**Total CommandProto usages found:** 42 locations across 9 files
**Eliminated:** 6 usages (14%) - ✅ **Phase 1 Complete**
**Can be eliminated:** ~14 usages (33%)
**Must keep (for now):** ~22 usages (53%)
---
## Files with CommandProto Usage
### 1. AICommandFilter.cpp (6 usages) - ✅ **COMPLETED** (PR #4505)
**Location:** Lines 146, 189, 252, 356, 387, 428
**Original usage:**
```cpp
const auto cmdProto = cmd.GetCommandProto();
if (!cmdProto.has_target()) { ... }
const auto& targetCoords = cmdProto.target();
if (!cmdProto.has_actor()) { ... }
const auto unitId = cmdProto.actor().value();
```
**Replaced with:**
```cpp
const int targetRow = cmd.GetTargetRow();
const int targetCol = cmd.GetTargetColumn();
if (targetRow < 0 || targetCol < 0) {
throw ShardokInternalErrorException("Command missing required target");
}
const Coords targetCoords(targetRow, targetCol);
const int actorId = cmd.GetActorUnitId();
if (actorId < 0) {
throw ShardokInternalErrorException("Command missing required actor");
}
```
**Status:****ELIMINATED** - Replaced with direct accessors + exception handling
**Impact:** Eliminated 6 proto conversions in hot path (command filtering)
**Completed:** Phase 1, PR #4505
---
### 2. ShardokAIClient.cpp (8 usages)
**Location:** Lines 83, 86, 87, 102, 105, 237, 261, 311, 356
**Usage breakdown:**
#### a) Command validation (lines 83-87)
```cpp
void CheckCommand(const CommandProto &realDescriptor, const CommandProto &guessedDescriptor) {
differencer.IgnoreField(CommandProto::descriptor()->FindFieldByNumber(
CommandProto::kFollowUpCommandTypesFieldNumber));
```
**Status:****MUST KEEP** - Uses protobuf reflection for comparison
**Reason:** Comparing proto messages for correctness checking requires proto API
#### b) GetAvailableCommandProtos calls (lines 105, 356)
```cpp
const auto guessedCommands = guessedEngine.GetAvailableCommandProtos(playerId, false);
if (const auto &availableCommands = engine.GetAvailableCommandProtos(playerId, false);
```
**Status:****CAN REPLACE** - Should use `GetAvailableCommandsForAIPlayer()` instead
**Impact:** This is a major conversion point - converts entire command list to protos
**Priority:** HIGH (converts all commands to proto unnecessarily)
#### c) Strategy selector methods (lines 102, 237, 261, 311)
```cpp
const vector<CommandProto> &realAvailableCommands) const -> CommandChoiceResults
```
**Status:****CAN REPLACE** - Depends on fixing strategy selector signatures
**Priority:** MEDIUM (depends on other refactors)
---
### 3. IterativeDeepeningAI.cpp/hpp (4 usages)
**Location:** Lines 41, 272 (cpp), 73, 96 (hpp)
**Current usage:**
```cpp
const std::vector<CommandProto>& commands,
```
**Status:****CAN REPLACE** - These methods should accept `CommandListSPtr` instead
**Impact:** Major - this is the main AI search algorithm
**Priority:** HIGH (core AI algorithm)
**Note:** IterativeDeepeningAI already receives commands as proto vectors. The conversion happens upstream at the entry point. Need to trace back to find where `GetAvailableCommandProtos` is called.
---
### 4. AIFleeDecisionCalculator.cpp/hpp (6 usages)
**Location:** Lines 17, 38, 39, 62, 63 (hpp), 18, 19, 137, 138 (cpp)
**Current usage:**
```cpp
const vector<CommandProto>& availableCommands,
const vector<CommandProto>::const_iterator& fleeCommand,
```
**Status:****CAN REPLACE** - Should use `CommandListSPtr` and indices instead
**Impact:** Flee decision logic could avoid proto conversion
**Priority:** MEDIUM
---
### 5. AIAttackerStrategySelector.cpp/hpp (2 usages)
**Location:** Line 30 in both files
**Current usage:**
```cpp
const vector<CommandProto>& availableCommands) -> AIStrategy
```
**Status:** ⚠️ **PARTIALLY REPLACEABLE** - Currently doesn't use the commands parameter
**Current implementation:**
```cpp
const vector<CommandProto>& /*availableCommands*/) -> AIStrategy {
// Parameter is commented out - not used!
return AIStrategy::DEFAULT;
}
```
**Priority:** LOW (parameter unused, but signature should be consistent)
---
### 6. AICommandEvaluator.hpp (1 usage)
**Location:** Line 27
**Current usage:**
```cpp
using CommandProto = net::eagle0::shardok::api::CommandDescriptor;
```
**Status:** ⚠️ **CHECK USAGE** - Type alias, need to check if used
**Priority:** LOW (just a type alias)
---
### 7. AIScoreCalculator.hpp (1 usage)
**Location:** Line 24
**Current usage:**
```cpp
using CommandProto = net::eagle0::shardok::api::CommandDescriptor;
```
**Status:** ⚠️ **CHECK USAGE** - Type alias, need to check if used
**Priority:** LOW (just a type alias)
---
### 8. AIWaterCrossingCommandChooser.hpp (1 usage)
**Location:** Line 20
**Current usage:**
```cpp
using CommandProto = net::eagle0::shardok::api::CommandDescriptor;
```
**Status:** ⚠️ **CHECK USAGE** - Type alias, need to check if used
**Priority:** LOW (just a type alias)
---
## Key Conversion Points (Entry Points)
### ShardokEngine::GetAvailableCommandProtos()
This method converts the entire command list from `CommandListSPtr` to `vector<CommandProto>`.
**Current flow:**
```
ShardokEngine::GetAvailableCommandsForAIPlayer() → CommandListSPtr
↓ (conversion)
ShardokEngine::GetAvailableCommandProtos() → vector<CommandProto>
AI algorithms (IterativeDeepeningAI, etc.)
```
**Desired flow:**
```
ShardokEngine::GetAvailableCommandsForAIPlayer() → CommandListSPtr
↓ (no conversion!)
AI algorithms use CommandSPtr directly
```
---
## Recommendations by Priority
### HIGH Priority (Performance-critical hot paths)
1. **AICommandFilter.cpp (6 usages)**
- Replace `cmd.GetCommandProto()` with direct accessor methods
- Use `GetActorUnitId()`, `GetTargetRow()`, `GetTargetColumn()`
- Impact: Eliminates 6 proto conversions per filtered command
2. **ShardokAIClient.cpp - GetAvailableCommandProtos calls**
- Replace calls to `GetAvailableCommandProtos()` with `GetAvailableCommandsForAIPlayer()`
- Impact: Eliminates conversion of entire command list
3. **IterativeDeepeningAI**
- Change signature from `vector<CommandProto>` to `CommandListSPtr`
- Impact: Main AI search algorithm avoids proto conversion
### MEDIUM Priority
4. **AIFleeDecisionCalculator**
- Change to use `CommandListSPtr` and indices
- Impact: Flee decision logic avoids proto
5. **ShardokAIClient strategy methods**
- Update signatures to use `CommandListSPtr`
- Cascades to strategy selectors
### LOW Priority
6. **Type aliases**
- Remove unused `using CommandProto` declarations
- Clean up imports
---
## Migration Strategy
### Phase 1: Low-hanging fruit (AICommandFilter) - ✅ **COMPLETED** (PR #4505)
- ✅ Replaced 6 proto conversions with direct accessor calls
- ✅ Added exception handling for missing actor/target data
- ✅ No signature changes needed
- ✅ Immediate performance benefit
- **PR:** #4505
### Phase 2: Entry point (ShardokAIClient)
- Replace `GetAvailableCommandProtos()` calls with `GetAvailableCommandsForAIPlayer()`
- Update method signatures in ShardokAIClient
### Phase 3: Core AI (IterativeDeepeningAI)
- Change IterativeDeepeningAI to accept `CommandListSPtr`
- This is the biggest change but has highest impact
### Phase 4: Supporting systems
- Update AIFleeDecisionCalculator
- Update strategy selectors
- Clean up type aliases
### Phase 5: Validation code
- Keep proto-based validation as-is (uses reflection)
- Consider if validation is still needed in production
---
## Notes
- **MCTS already converted**: The MCTS code path already uses `CommandListSPtr` directly
- **Proto still needed**: For serialization/network communication (not in AI hot path)
- **Validation**: Proto comparison in CheckCommand() should remain (uses proto reflection)
---
## Estimated Impact
**Proto conversions eliminated:** ~20-25 per command choice
**Performance gain:** Eliminates hundreds of allocations per AI decision
**Code simplification:** Removes proto conversion layer from AI
**Before:**
```
Command → Proto → AI Decision
```
**After:**
```
Command → AI Decision (direct)
```
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# Deproto Migration Plan
## Vision
**Protocol buffers should only be used at the edges** — for network serialization (gRPC) and disk persistence. Inside the Eagle game engine, all logic should operate on native Scala models.
```
┌─────────────────────────────────────────────────────────────────────┐
│ GRPC BOUNDARY │
│ EagleServiceImpl.scala ←→ Proto Messages ←→ Unity Client │
└─────────────────────────────────────────────────────────────────────┘
GameStateConverter
┌─────────────────────────────────────────────────────────────────────┐
│ SCALA ENGINE │
│ │
│ GameStateC ───→ Actions ───→ ActionResultT ───→ New GameStateC │
│ ↑ │ │
│ │ (Pure Scala models) │ │
│ └───────────────────────────────────────────────────┘ │
│ │
│ HeroC, FactionC, ProvinceC, BattalionC, ArmyC, etc. │
└─────────────────────────────────────────────────────────────────────┘
GameStateConverter
┌─────────────────────────────────────────────────────────────────────┐
│ PERSISTENCE BOUNDARY │
│ GameHistory.scala ←→ Proto Messages ←→ File/Database │
└─────────────────────────────────────────────────────────────────────┘
```
---
## Current State
### Completed Phases
| Phase | Status | Summary |
|-------|--------|---------|
| Phase 1: GameStateC | **Complete** | Scala `GameState` model with 22 fields |
| Phase 2: EngineImpl | **Complete** | Holds Scala `GameState` internally |
| Phase 3: GameHistory | **Complete** | `stateAfter` returns Scala GameState |
| Phase 4: ActionResultT | **Complete** | All 59 actions return `ActionResultT` |
| Phase 5: Action Base Classes | **Complete** | All `RandomSequentialResultsAction` and `DeterministicSingleResultAction` converted to T-type base classes |
| Phase 5b: Base Class Cleanup | **Complete** | `RandomSequentialResultsAction` and `DeterministicSingleResultAction` deleted |
| Phase 5c: RoundPhaseAdvancer Actions | **Complete** | All actions called by RoundPhaseAdvancer accept Scala GameState |
| Phase 5d: RoundPhaseAdvancer Itself | **Complete** | RoundPhaseAdvancer.checkForPhaseAdvancement takes Scala GameState |
### Phase 5c/5d Progress (Complete)
`RoundPhaseAdvancer.checkForPhaseAdvancement` now accepts Scala `GameState` and `ActionResultApplier` directly (PR #4677).
| Action | PR | Status |
|--------|-----|--------|
| `PrisonerExchangeAction` | #4670 | ✅ Merged |
| `PerformForcedTurnBackAction` | #4671 | ✅ Merged |
| `PerformHeroDeparturesAction` | #4672 | ✅ Merged |
| `RequestFreeForAllBattlesAction` | #4673 | ✅ Merged |
| `EndPlayerCommandsPhaseAction` | #4674 | ✅ Merged |
| `EndDiplomacyResolutionPhaseAction` | #4675 | ✅ Merged |
| `RoundPhaseAdvancer` itself | #4677 | ✅ Merged |
### EngineImpl Progress
| Change | PR | Status |
|--------|-----|--------|
| `recursiveTransform` deleted | #4677 | ✅ Merged |
| `recursiveTransformT` uses `RandomStateTSequencer` | #4677 | ✅ Merged |
### Current Architecture
**ActionResultT Production (100% Complete):**
- All actions produce `ActionResultT`
- Conversion to `ActionResultProto` happens via `ActionResultProtoConverter.toProto()`
- No direct `ActionResultProto` construction outside the converter
**ActionResultProto Consumption (Next Target):**
- `ActionResultProtoApplierImpl` - applies proto results to proto GameState
- `RoundPhaseAdvancer` - calls converter, passes protos to applier
- `InMemoryHistory` / `PersistedHistory` - stores proto results
- Service layer (`GameController`, `GamesManager`, etc.) - uses proto for client communication
---
## Phase 6: Migrate to ActionResultT Consumers
### Objective
Eliminate internal consumption of `ActionResultProto`. Everything inside the engine should work with `ActionResultT`.
### Current Flow (Proto-Heavy)
```
Action.execute()
→ ActionResultT
→ ActionResultProtoConverter.toProto()
→ ActionResultProto
→ ActionResultProtoApplierImpl.applyActionResults()
→ GameStateProto
→ GameStateConverter.fromProto()
→ GameStateC
```
### Target Flow (T-Types Throughout)
```
Action.execute()
→ ActionResultT
→ ActionResultApplier.applyActionResults()
→ GameStateC
(Proto conversion only at boundaries)
```
### Key Files to Convert
**Tier 1 - Core Applier:****Complete**
```
src/main/scala/net/eagle0/eagle/library/actions/applier/ActionResultApplierImpl.scala
```
`ActionResultApplier` applies `ActionResultT` directly to Scala `GameState`. The legacy `ActionResultTApplierImpl` wraps it and converts to/from proto for callers that still need proto types.
**Tier 2 - RoundPhaseAdvancer:****Complete**
```
src/main/scala/net/eagle0/eagle/library/RoundPhaseAdvancer.scala
```
Now accepts Scala `GameState` and `ActionResultApplier`. Only converts to proto lazily for `AvailableCommandsFactory` calls.
**Tier 3 - Sequencers:**
```
src/main/scala/net/eagle0/eagle/library/actions/impl/common/RandomStateTSequencer.scala
src/main/scala/net/eagle0/eagle/library/actions/impl/common/RandomStateProtoSequencer.scala
```
Modify `RandomStateTSequencer` to thread Scala `GameState` throughout (currently converts to proto internally). Then evaluate whether `RandomStateProtoSequencer` is still needed at all.
**Current State**: `RandomStateTSequencer` accepts Scala `GameState` via its `apply()` method but internally converts to proto. All callback methods (`withRandomActionResult`, `withActionResults`, etc.) pass `GameStateProto` to callers, forcing actions that use the sequencer to work with proto types internally.
**Target State**: Create a fully protoless sequencer where:
1. `lastState` returns Scala `GameState` (not `lastStateProto`)
2. All callback methods pass Scala `GameState` to callers
3. Actions using the sequencer can be fully protoless
**Migration Path**:
1. Add `lastState: GameState` method alongside `lastStateProto` (non-breaking)
2. Add parallel callback methods that pass Scala GameState (e.g., `withScalaActionResult`)
3. Migrate actions one by one to use the new Scala-based callbacks
4. Once all actions migrated, deprecate/remove proto-based callbacks
5. Remove `lastStateProto` once no longer used
**RandomStateSequencer Migration Progress** (PR #4679 introduced protoless `RandomStateSequencer`):
| Action | Status |
|--------|--------|
| `TruceTurnBackPhaseAction` | ✅ Migrated (PR #4680) |
| `EndHandleRiotsPhaseAction` | ✅ Migrated (PR #4684) |
| `PerformVassalCommandsPhaseAction` | ✅ Migrated |
| `PerformVassalDefenseDecisionsAction` | ✅ Migrated |
| `EndVassalCommandsPhaseAction` | ✅ Migrated |
| `PerformReconResolutionAction` | ✅ Migrated |
| `NewRoundAction` | ✅ Migrated (PR #4698) |
| `EndBattleAftermathPhaseAction` | ✅ Migrated (PR #4699) |
| `EndDiplomacyResolutionPhaseAction` | ✅ Migrated |
| `PerformUnaffiliatedHeroesAction` | ✅ Migrated |
| `EngineImpl.recursiveTransformT` | ✅ Migrated (PR #4704) |
| `ProtolessSequentialResultsActionWrapper` | ✅ Migrated (PR #4705) |
| `LegacyRandomStateTSequencer` | ✅ **Deleted** (PR #4705) |
**TCommandFactory Extraction** (PR #4684):
To enable lightweight mocking of command creation in tests, `TCommandFactory` trait was extracted from `CommandFactory`. This allows tests to mock just the `makeTCommand` method without pulling in all 40+ command dependencies that `CommandFactory` requires.
- `TCommandFactory` - lightweight trait with just `makeTCommand`
- `CommandFactory extends TCommandFactory` - maintains backward compatibility
- Actions accepting command factories now use `TCommandFactory` type for better testability
**Tier 4 - History APIs:**
```
src/main/scala/net/eagle0/eagle/service/InMemoryHistory.scala
src/main/scala/net/eagle0/eagle/service/PersistedHistory.scala
```
Change APIs to vend Scala `GameState` and `ActionResultT` instead of proto versions. `PersistedHistory` converts to proto internally for disk persistence; `InMemoryHistory` doesn't need proto at all.
### ActionResultProto Consumer Inventory
| File | Usage | Status |
|------|-------|--------|
| `ActionResultApplierImpl.scala` | Applies ActionResultT to Scala GameState | ✅ **Complete** |
| `ActionResultTApplierImpl.scala` | Legacy wrapper - converts to/from proto | Keep until all callers migrated |
| `RoundPhaseAdvancer.scala` | Uses Scala GameState | ✅ **Complete** |
| `RandomStateSequencer.scala` | Threads Scala GameState | ✅ **Complete** |
| `VigorXPApplier.scala` | Has both proto and Scala methods | Scala method exists, delete proto method when unused |
| `PerformForcedTurnBackAction.scala` | Fully protoless | ✅ **Complete** |
| `ResolveBattleAction.scala` | Heavy proto usage | Blocked by proto dependencies |
| `InMemoryHistory.scala` | Stores proto results | Pending - vend Scala types |
| `PersistedHistory.scala` | Stores proto results | Pending - vend Scala types, convert for disk |
| `GameController.scala` | Uses proto for client communication | Keep proto (gRPC boundary) |
### Remaining Proto Usage in Actions
**Progress: 46 of 52 action files (88%) are fully protoless.**
The following 6 actions still have proto usage:
| Action | Proto Usages | Blocker | Effort |
|--------|--------------|---------|--------|
| `ResolveBattleAction` | 24 | Shardok interface, complex battle logic | High |
| `PerformVassalCommandsPhaseAction` | 3 | `CommandChoiceHelpers` takes proto GameState | Medium |
| `EndHandleRiotsPhaseAction` | 2 | `CommandChoiceHelpers` takes proto GameState | Medium |
| `PerformVassalDefenseDecisionsAction` | 2 | `CommandChoiceHelpers` takes proto GameState | Medium |
| `EndVassalCommandsPhaseAction` | 1 | `CommandChoiceHelpers` takes proto GameState | Medium |
| `NewRoundAction` | 1 | `ChronicleEventGenerator` returns proto | Medium |
**Deleted Dead Code:**
- `UnaffiliatedHeroMovedAction` - Was never called; `PerformUnaffiliatedHeroesAction.heroMovedResult` constructs `ActionResultC` directly
- `HeroBackstoryUpdateActionGenerator.fromGameState` - Dead method that converted proto to Scala; only `apply(GameState)` is used
**Note**: `PerformReconResolutionAction` and `EndBattleAftermathPhaseAction` are now fully protoless after:
1. Migrating `FactionT.reconnedProvinces` and `ChangedFactionC.updatedReconnedProvinces` to use Scala `ProvinceView`
2. Adding Scala overload of `ProvinceViewFilter.withdrawnFromProvinceView`
### Estimated Effort (Remaining)
| Component | Lines | Complexity | Blocks |
|-----------|-------|------------|--------|
| `CommandChoiceHelpers` to Scala | ~2000 | High | 4 vassal actions |
| `ResolveBattleAction` refactor | ~500 | High | 1 action (complex) |
| `ChronicleEventGenerator` to Scala | ~400 | Medium | 1 action |
| History API updates | ~100 | Low | - |
| **Total Remaining** | **~3000** | | |
### Progress Summary
| Metric | Value |
|--------|-------|
| Action files fully protoless | 46 / 52 (88%) |
| Proto usages in remaining actions | 33 total |
| Biggest blocker | `ResolveBattleAction` (24 usages) |
| Second biggest blocker | `CommandChoiceHelpers` (blocks 4 actions) |
### Validation
- [x] `ActionResultApplier` created and tested
- [x] `RandomStateSequencer` threads Scala GameState throughout
- [x] `RoundPhaseAdvancer` uses T-types internally
- [x] `ProvinceViewFilter` has Scala overload for server-side use (PR #4752)
- [x] `FactionT.reconnedProvinces` and `ChangedFactionC.updatedReconnedProvinces` use Scala `ProvinceView`
- [x] `ProvinceViewFilter.withdrawnFromProvinceView` has Scala overload
- [ ] `ProvinceViewFilter` faction-filtered views use Scala types
- [ ] `CommandChoiceHelpers` uses Scala types
- [ ] History APIs vend Scala types
- [ ] No `ActionResultProtoConverter.toProto()` calls except at persistence/gRPC boundaries
- [ ] All tests pass
---
## Phase 7: Clean Up Legacy Utilities
### Objective
Remove remaining direct proto imports from utility classes.
### Files to Modify
| File | Status |
|------|--------|
| `CommandChoiceHelpers.scala` | Accepts proto `GameState`; blocks full deproto of `PerformVassalCommandsPhaseAction` and `PerformVassalDefenseDecisionsAction` |
| `LegacyProvinceUtils.scala` | Replace with `ProvinceUtils.scala` - `hasImminentRiot` added (PR #4683) |
| `LegacyFactionUtils.scala` | Replace proto imports with `FactionT` |
| `LegacyUnaffiliatedHeroUtils.scala` | Replace proto imports with Scala models |
| `BattalionTypeLoader.scala` | Keep proto for file loading, convert immediately after |
| `BeastUtils.scala` | **Complete** - now uses Scala `BeastInfo` only |
### View Filters (Partially Complete)
The view filter utilities now have Scala overloads for server-side use:
| File | Status | Notes |
|------|--------|-------|
| `ProvinceViewFilter.scala` | **Partial** | `filteredProvinceView(ProvinceT, ScalaGameState)` added (PR #4752) |
| `ArmyFilter.scala` | **Partial** | `filterArmy(ScalaArmy, Map[BattalionId, BattalionT], Option[FactionId])` added |
| `BattalionViewFilter.scala` | **Complete** | Uses Scala `BattalionT` throughout |
| `GameStateViewFilter.scala` | Pending | Uses proto types throughout |
| `GameStateViewDiffer.scala` | Pending | Works with view protos |
**Unblocked Actions** (PR #4752):
- `EndBattleAftermathPhaseAction` - can now use `filteredProvinceView(province, scalaGameState)`
- `PerformReconResolutionAction` - can now use Scala overload
- `GameStateFactionExtensions` - can now use `updatedReconnedProvinces` with Scala types
**Remaining Work**:
- Faction-filtered `filteredProvinceView(Province, GameState, FactionId)` still uses proto types
- `withdrawnFromProvinceView` still uses proto types
- These are needed for client-facing views with visibility restrictions
---
## Phase 8: Verify Boundaries
### Objective
Confirm protos are used correctly at boundaries — and ONLY there.
### Expected Proto Usage (Keep)
- `EagleServiceImpl.scala` - gRPC boundary
- `InMemoryHistory.scala` / `PersistedHistory.scala` - Persistence boundary
- `*Converter.scala` - Explicit conversion utilities
- `*Loader.scala` - File loading utilities
### Expected No Proto Usage (Verify)
- `/library/actions/impl/` - Pure Scala models
- `/library/util/` - Pure Scala models (except loaders)
- `/model/state/` - Pure Scala models
---
## Open Questions
1. **Persistence Format**: Currently game state is persisted as proto. Should we keep proto for persistence (good for schema evolution) or switch to a different format?
2. **Shardok Integration**: `ResolveBattleAction` communicates with Shardok. Should the Shardok interface use protos (external service) or Scala models?
3. **View Generation**: `GameStateViewDiffer` works with view protos for client updates. Views need Scala models (`ProvinceViewT`, etc.) to allow actions like `EndBattleAftermathPhaseAction` to be fully protoless. The Scala views would be converted to proto only at the gRPC boundary when sending updates to clients.
---
## Success Criteria
### Code Quality
- [ ] Zero proto imports in `/library/actions/` (except boundaries)
- [ ] Zero proto imports in `/library/` utilities (except loaders)
- [ ] `GameStateT` used throughout engine internals
- [ ] Proto usage limited to: `EagleServiceImpl`, loaders, converters, persistence
### Architecture
- [ ] Clear separation: Scala models (internal) vs Proto (boundaries)
- [ ] Converters as the only bridge between domains
- [ ] No "proto creep" into business logic
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# Eagle0 Productionization Plan
## Executive Summary
This document outlines a plan to move Eagle0's Eagle and Shardok servers from a home Mac to cloud infrastructure while maintaining a QA environment on the Mac. The architecture uses DigitalOcean (already integrated via Spaces) with containerized deployments and GitHub Actions CI/CD.
## Current Architecture
```
Unity Client
│ gRPC/TLS (eagle0.net:443)
nginx (home Mac, via router port forward)
├─► Eagle Server (Scala/JVM, port 40032)
│ │
│ │ internal gRPC (port 40042)
│ ▼
└─► Shardok Server (C++, port 40042/40052)
Storage: DigitalOcean Spaces (sfo3.digitaloceanspaces.com)
DNS: eagle0.net → home IP
```
**Key observations:**
- Already using DigitalOcean Spaces for S3-compatible storage
- Eagle server is a deployable JAR (`eagle_server_deploy.jar`)
- Shardok server is a native C++ binary
- nginx handles TLS termination and gRPC routing
- Self-hosted GitHub Actions runner on Mac
---
## Target Architecture
### Production Environment (DigitalOcean)
```
Unity Client
├─► eagle0.net (Production)
│ │
│ ▼
│ DigitalOcean Load Balancer (TLS termination)
│ │
│ ▼
│ ┌─────────────────────────────────────┐
│ │ DigitalOcean Droplet(s) │
│ │ ┌─────────────┬─────────────────┐ │
│ │ │ Eagle │ Shardok │ │
│ │ │ (Docker) │ (Docker) │ │
│ │ │ :40032 │ :40042 │ │
│ │ └─────────────┴─────────────────┘ │
│ └─────────────────────────────────────┘
└─► qa.eagle0.net (QA - home Mac, unchanged)
nginx → Eagle/Shardok (current setup)
```
### Environment Switching
The Unity client already supports configurable server URLs via the connection screen:
- **Production:** `eagle0.net` (default)
- **QA:** `qa.eagle0.net`
No client code changes needed - users can simply type the desired URL.
---
## Cloud Provider: DigitalOcean
### Why DigitalOcean
1. **Already integrated** - S3 Spaces storage at `sfo3.digitaloceanspaces.com` with credentials configured
2. **Simple pricing** - Predictable monthly costs, no surprise bills
3. **Good performance** - SFO3 datacenter is geographically close
4. **Managed services** - Load balancers, managed databases if needed later
5. **Not AWS/GCP** - Per your requirements
### Alternative Considered: Hetzner
- Cheaper for compute ($3.29/mo for 2 vCPU/4GB vs DO's $24/mo)
- Good EU presence but less US coverage
- No managed load balancer (need to run HAProxy/nginx yourself)
- **Recommendation:** Start with DigitalOcean for simplicity; migrate to Hetzner later if cost becomes a concern
---
## Infrastructure Components
### 1. Compute: DigitalOcean Droplets
#### Resource Characteristics
- **Eagle server:** CPU-light, possibly RAM-heavy (JVM). Should be always available.
- **Shardok server:** Very CPU-heavy (AI algorithms). Only needed during tactical battles. Startup time <1 second.
#### Recommended: On-Demand Shardok (Same Droplet)
For current low player count, optimize for cost while maintaining availability:
| Component | Config | Monthly Cost |
|-----------|--------|--------------|
| Droplet | s-2vcpu-4gb | $24/mo |
| Eagle | Always running | - |
| Shardok | Started on-demand by Eagle, stopped after idle | - |
**How it works:**
1. Eagle server runs 24/7 - game is always available
2. When battle starts, Eagle launches Shardok container/process (<1s startup)
3. After battle ends, idle timer starts (e.g., 5 minutes)
4. On timeout, Eagle stops Shardok to free CPU
5. If new battle starts during idle period, Shardok is already warm
**Shardok lifecycle management** (implement in Eagle):
```scala
// Pseudocode for Eagle's Shardok management
object ShardokManager {
private var process: Option[Process] = None
private var idleTimer: Option[Timer] = None
def ensureRunning(): Unit = {
cancelIdleTimer()
if (process.isEmpty) {
process = Some(startShardokProcess())
waitForHealthCheck()
}
}
def onBattleEnd(): Unit = {
idleTimer = Some(scheduleShutdown(5.minutes))
}
private def shutdown(): Unit = {
process.foreach(_.destroy())
process = None
}
}
```
#### Future Scaling Options
As player count grows and Shardok needs more CPU:
**Option A: Upgrade Droplet (Simplest)**
| Droplet | vCPU | RAM | Cost | Use Case |
|---------|------|-----|------|----------|
| s-2vcpu-4gb | 2 shared | 4 GB | $24/mo | Current (few players) |
| s-4vcpu-8gb | 4 shared | 8 GB | $48/mo | Moderate usage |
| c-4 | 4 dedicated | 8 GB | $84/mo | CPU-intensive battles |
| c-8 | 8 dedicated | 16 GB | $168/mo | Multiple concurrent battles |
**Option B: Separate Shardok Droplet (API-driven)**
For heavy Shardok usage with cost optimization:
- **Eagle droplet:** s-1vcpu-2gb ($12/mo) - always on
- **Shardok droplet:** Created on-demand via DigitalOcean API
- c-4 CPU-optimized: $0.125/hour
- c-8 CPU-optimized: $0.25/hour
- Created when battle starts, destroyed after idle
- 30-60s droplet creation time (acceptable if battles are requested in advance)
**Option C: Fly.io for Shardok (Scale-to-Zero)**
For true pay-per-use with fast cold starts:
- **Eagle:** DigitalOcean or Fly.io (~$10/mo)
- **Shardok:** Fly.io with auto-scaling
- Performance VMs: ~$0.0000022/second when running
- Cold start: 2-5 seconds
- Scales to zero when idle
Requires learning Fly.io but offers best cost efficiency for sporadic usage.
**Option D: Multiple Shardok Instances (High Scale)**
For many concurrent battles:
- **Eagle:** Dedicated droplet with more RAM
- **Shardok pool:** Multiple Shardok containers/droplets
- Eagle routes battles to available Shardok instances
- Could use Kubernetes or Docker Swarm for orchestration
This is overkill for now but documented for future reference.
### 2. Load Balancer
**DigitalOcean Load Balancer:** $12/mo
- TLS termination with Let's Encrypt
- Health checks
- Sticky sessions (if needed)
- Can add more droplets later for HA
**Alternative:** Run nginx on the droplet for $0 extra, but lose automatic failover.
### 3. Storage
**Already configured:** DigitalOcean Spaces
- Bucket: `eagle0`
- Region: `sfo3`
- Used for game saves and assets
- Cost: $5/mo base + $0.02/GB storage + $0.01/GB transfer
### 4. DNS
**Option A: DigitalOcean DNS (Recommended)**
- Free with droplets
- Easy integration
- API for automated updates
**Option B: Keep current DNS provider**
- Update A records manually or via script
**DNS Records:**
```
eagle0.net A <DO Load Balancer IP>
qa.eagle0.net A <Home IP> (unchanged)
*.eagle0.net A <DO Load Balancer IP> (wildcard for future)
```
### 5. Firewall
**DigitalOcean Cloud Firewall:** Free
```
Inbound Rules:
- TCP 443 (HTTPS/gRPC) from anywhere → Load Balancer
- TCP 22 (SSH) from your IP only → Droplets
- TCP 40032 (Eagle) from Load Balancer only
- TCP 40042 (Shardok) from Load Balancer only
Outbound Rules:
- All traffic allowed (for external APIs, Spaces, etc.)
```
---
## Container Strategy
### Dockerfiles
**Eagle Server** (`ci/eagle_run.Dockerfile` - already exists, needs enhancement):
```dockerfile
FROM eclipse-temurin:17-jre-alpine
# Add non-root user
RUN addgroup -S eagle && adduser -S eagle -G eagle
WORKDIR /app
# Copy the deploy JAR
COPY --chown=eagle:eagle deploy/eagle_server_deploy.jar ./
# Copy game resources
COPY --chown=eagle:eagle src/main/resources/net/eagle0/eagle/ ./resources/
USER eagle
# Health check
HEALTHCHECK --interval=30s --timeout=10s --retries=3 \
CMD wget -q --spider http://localhost:40032/health || exit 1
EXPOSE 40032
ENTRYPOINT ["java", "-Xmx4g", "-jar", "eagle_server_deploy.jar"]
CMD ["--eagle-grpc-port", "40032", "--shardok-interface-remote-address", "shardok:40042", "--gpt-model-name", "gpt-4"]
```
**Shardok Server** (new: `ci/shardok_run.Dockerfile`):
```dockerfile
FROM ubuntu:22.04
# Install runtime dependencies
RUN apt-get update && apt-get install -y \
libstdc++6 \
ca-certificates \
&& rm -rf /var/lib/apt/lists/*
# Add non-root user
RUN useradd -r -s /bin/false shardok
WORKDIR /app
# Copy the Shardok binary and dependencies
COPY --chown=shardok:shardok bazel-bin/src/main/cpp/net/eagle0/shardok/shardok-server ./
COPY --chown=shardok:shardok src/main/resources/net/eagle0/shardok/maps/ ./maps/
USER shardok
# Health check (need to implement gRPC health endpoint)
HEALTHCHECK --interval=30s --timeout=10s --retries=3 \
CMD ./shardok-server --health-check || exit 1
EXPOSE 40042 40052
ENTRYPOINT ["./shardok-server"]
```
**Docker Compose** (new: `docker-compose.prod.yml`):
```yaml
version: '3.8'
services:
eagle:
build:
context: .
dockerfile: ci/eagle_run.Dockerfile
image: eagle0/eagle-server:${VERSION:-latest}
ports:
- "40032:40032"
environment:
- SHARDOK_ADDRESS=shardok:40042
- GPT_MODEL_NAME=${GPT_MODEL_NAME:-gpt-4}
- JAVA_OPTS=-Xmx4g -XX:+UseG1GC
depends_on:
- shardok
restart: unless-stopped
logging:
driver: "json-file"
options:
max-size: "100m"
max-file: "5"
shardok:
build:
context: .
dockerfile: ci/shardok_run.Dockerfile
image: eagle0/shardok-server:${VERSION:-latest}
ports:
- "40042:40042"
- "40052:40052"
restart: unless-stopped
logging:
driver: "json-file"
options:
max-size: "100m"
max-file: "5"
```
---
## Build Pipeline
### Build Artifacts
The build process produces:
1. **Eagle JAR:** `bazel-bin/src/main/scala/net/eagle0/eagle/eagle_server_deploy.jar`
2. **Shardok binary:** `bazel-bin/src/main/cpp/net/eagle0/shardok/shardok-server`
### Cross-Compilation for Linux
Current builds target macOS (self-hosted runner). For production Linux deployment:
**Option A: Build in Docker (Recommended)**
```bash
# Build Eagle (JVM - platform independent)
bazel build //src/main/scala/net/eagle0/eagle:eagle_server_deploy.jar
# Build Shardok in Linux container
docker run --rm -v $(pwd):/workspace -w /workspace \
ubuntu:22.04 \
bash -c "apt-get update && apt-get install -y build-essential && bazel build -c opt //src/main/cpp/net/eagle0/shardok:shardok-server"
```
**Option B: Use GitHub-hosted Linux runner**
- Add `runs-on: ubuntu-latest` workflow
- Builds directly on Linux
- May need to cache Bazel to avoid long build times
**Option C: Cross-compile on Mac**
- Configure Bazel for Linux cross-compilation
- More complex setup but faster iteration
**Recommendation:** Option A (Docker build) for Shardok, since Eagle's JAR is platform-independent.
---
## CI/CD Pipeline
### GitHub Actions Workflows
**New workflow:** `.github/workflows/deploy_production.yml`
```yaml
name: Deploy to Production
on:
push:
branches: [main]
paths:
- 'src/main/scala/**'
- 'src/main/cpp/**'
- 'src/main/protobuf/**'
- 'ci/*.Dockerfile'
- 'docker-compose.prod.yml'
workflow_dispatch:
inputs:
environment:
description: 'Deployment environment'
required: true
default: 'production'
type: choice
options:
- production
- staging
env:
REGISTRY: registry.digitalocean.com
EAGLE_IMAGE: eagle0/eagle-server
SHARDOK_IMAGE: eagle0/shardok-server
jobs:
build-eagle:
runs-on: self-hosted
outputs:
version: ${{ steps.version.outputs.version }}
steps:
- uses: actions/checkout@v4
with:
lfs: false
- name: Set version
id: version
run: echo "version=$(git rev-parse --short HEAD)" >> $GITHUB_OUTPUT
- name: Build Eagle server JAR
run: bazel build //src/main/scala/net/eagle0/eagle:eagle_server_deploy.jar
- name: Copy artifacts
run: |
mkdir -p deploy
cp bazel-bin/src/main/scala/net/eagle0/eagle/eagle_server_deploy.jar deploy/
- name: Build Docker image
run: |
docker build -f ci/eagle_run.Dockerfile -t ${{ env.REGISTRY }}/${{ env.EAGLE_IMAGE }}:${{ steps.version.outputs.version }} .
docker tag ${{ env.REGISTRY }}/${{ env.EAGLE_IMAGE }}:${{ steps.version.outputs.version }} ${{ env.REGISTRY }}/${{ env.EAGLE_IMAGE }}:latest
- name: Push to registry
run: |
echo "${{ secrets.DO_REGISTRY_TOKEN }}" | docker login ${{ env.REGISTRY }} -u ${{ secrets.DO_REGISTRY_TOKEN }} --password-stdin
docker push ${{ env.REGISTRY }}/${{ env.EAGLE_IMAGE }}:${{ steps.version.outputs.version }}
docker push ${{ env.REGISTRY }}/${{ env.EAGLE_IMAGE }}:latest
build-shardok:
runs-on: ubuntu-latest
needs: []
steps:
- uses: actions/checkout@v4
with:
lfs: false
- name: Set version
id: version
run: echo "version=$(git rev-parse --short HEAD)" >> $GITHUB_OUTPUT
- name: Set up Bazel
uses: bazelbuild/setup-bazelisk@v2
- name: Cache Bazel
uses: actions/cache@v3
with:
path: ~/.cache/bazel
key: bazel-linux-${{ hashFiles('MODULE.bazel', 'WORKSPACE') }}
- name: Build Shardok server
run: bazel build -c opt //src/main/cpp/net/eagle0/shardok:shardok-server
- name: Build Docker image
run: |
mkdir -p bazel-bin/src/main/cpp/net/eagle0/shardok/
cp bazel-bin/src/main/cpp/net/eagle0/shardok/shardok-server bazel-bin/src/main/cpp/net/eagle0/shardok/
docker build -f ci/shardok_run.Dockerfile -t ${{ env.REGISTRY }}/${{ env.SHARDOK_IMAGE }}:${{ steps.version.outputs.version }} .
docker tag ${{ env.REGISTRY }}/${{ env.SHARDOK_IMAGE }}:${{ steps.version.outputs.version }} ${{ env.REGISTRY }}/${{ env.SHARDOK_IMAGE }}:latest
- name: Push to registry
run: |
echo "${{ secrets.DO_REGISTRY_TOKEN }}" | docker login ${{ env.REGISTRY }} -u ${{ secrets.DO_REGISTRY_TOKEN }} --password-stdin
docker push ${{ env.REGISTRY }}/${{ env.SHARDOK_IMAGE }}:${{ steps.version.outputs.version }}
docker push ${{ env.REGISTRY }}/${{ env.SHARDOK_IMAGE }}:latest
deploy:
runs-on: ubuntu-latest
needs: [build-eagle, build-shardok]
environment: production
steps:
- uses: actions/checkout@v4
- name: Deploy to DigitalOcean
uses: appleboy/ssh-action@v1.0.0
with:
host: ${{ secrets.DO_DROPLET_IP }}
username: deploy
key: ${{ secrets.DO_SSH_KEY }}
script: |
cd /opt/eagle0
# Pull latest images
docker-compose -f docker-compose.prod.yml pull
# Rolling restart (zero-downtime if load balancer configured)
docker-compose -f docker-compose.prod.yml up -d --remove-orphans
# Wait for health checks
sleep 30
docker-compose -f docker-compose.prod.yml ps
# Cleanup old images
docker image prune -f
- name: Verify deployment
run: |
# Health check endpoint
curl -f https://eagle0.net/health || exit 1
- name: Notify on failure
if: failure()
run: |
# Add Slack/Discord notification here
echo "Deployment failed!"
```
### Deployment Steps
1. **On push to main:**
- Build Eagle JAR (self-hosted Mac runner - for consistency)
- Build Shardok binary (GitHub-hosted Ubuntu runner)
- Build Docker images
- Push to DigitalOcean Container Registry
2. **Deploy to droplet:**
- SSH to production server
- Pull new images
- `docker-compose up -d` (rolling update)
- Verify health checks
3. **Rollback:**
```bash
# On server
docker-compose -f docker-compose.prod.yml down
docker-compose -f docker-compose.prod.yml pull eagle0/eagle-server:<previous-version>
docker-compose -f docker-compose.prod.yml up -d
```
---
## Server Setup
### Initial Droplet Setup
```bash
#!/bin/bash
# Run on fresh DigitalOcean droplet (Ubuntu 22.04)
# Update system
apt-get update && apt-get upgrade -y
# Install Docker
curl -fsSL https://get.docker.com | sh
systemctl enable docker
systemctl start docker
# Install Docker Compose
apt-get install -y docker-compose-plugin
# Create deploy user
useradd -m -s /bin/bash -G docker deploy
mkdir -p /home/deploy/.ssh
# Add your SSH public key to /home/deploy/.ssh/authorized_keys
# Create app directory
mkdir -p /opt/eagle0
chown deploy:deploy /opt/eagle0
# Configure Docker to use DigitalOcean Container Registry
docker login registry.digitalocean.com
# Create systemd service for auto-start
cat > /etc/systemd/system/eagle0.service << 'EOF'
[Unit]
Description=Eagle0 Game Servers
Requires=docker.service
After=docker.service
[Service]
Type=oneshot
RemainAfterExit=yes
WorkingDirectory=/opt/eagle0
ExecStart=/usr/bin/docker compose -f docker-compose.prod.yml up -d
ExecStop=/usr/bin/docker compose -f docker-compose.prod.yml down
User=deploy
Group=deploy
[Install]
WantedBy=multi-user.target
EOF
systemctl enable eagle0
```
### Load Balancer Configuration
**DigitalOcean Load Balancer settings:**
- **Forwarding Rules:**
- HTTPS 443 → HTTP 40032 (Eagle)
- gRPC is HTTP/2, handled automatically
- **Health Checks:**
- Protocol: HTTP
- Port: 40032
- Path: `/health` (need to implement)
- **SSL:**
- Let's Encrypt certificate for `eagle0.net`
- **Settings:**
- Sticky sessions: Disabled (gRPC streams handle this)
- Proxy protocol: Disabled
### nginx on Droplet (Alternative to LB)
If using nginx instead of managed LB:
```nginx
# /etc/nginx/sites-available/eagle0
upstream eagle_backend {
server 127.0.0.1:40032;
keepalive 100;
}
server {
listen 443 ssl http2;
server_name eagle0.net;
ssl_certificate /etc/letsencrypt/live/eagle0.net/fullchain.pem;
ssl_certificate_key /etc/letsencrypt/live/eagle0.net/privkey.pem;
# gRPC settings
location /net.eagle0.eagle.api.Eagle {
grpc_pass grpc://eagle_backend;
grpc_read_timeout 1200s;
grpc_send_timeout 1200s;
grpc_socket_keepalive on;
# Rate limiting
limit_req zone=eagle burst=50 nodelay;
}
# Health check endpoint
location /health {
proxy_pass http://127.0.0.1:40032/health;
}
}
# Rate limit zone
limit_req_zone $binary_remote_addr zone=eagle:10m rate=100r/s;
```
---
## Environment Configuration
### Secrets Management
**GitHub Secrets (for CI/CD):**
- `DO_REGISTRY_TOKEN` - DigitalOcean Container Registry token
- `DO_DROPLET_IP` - Production server IP
- `DO_SSH_KEY` - SSH private key for deployment
- `OPENAI_API_KEY` - For LLM integration (if used in production)
- `DO_SPACES_KEY` - Already exists for S3
**On Server (environment variables):**
```bash
# /opt/eagle0/.env
GPT_MODEL_NAME=gpt-4
OPENAI_API_KEY=sk-...
DO_SPACES_KEY=...
DO_SPACES_SECRET=...
JAVA_OPTS=-Xmx4g -XX:+UseG1GC -XX:MaxGCPauseMillis=200
```
### Configuration Files
**Production config** (`/opt/eagle0/config/eagle0.conf`):
```
monteCarloIterations = 150000
monteCarloThreads = 4
grpcAddress = 0.0.0.0:40042
```
---
## Monitoring & Observability
### Logging
**Docker logging driver:** json-file with rotation
- Logs stored in `/var/lib/docker/containers/<id>/`
- Max 5 files of 100MB each
**Log aggregation options:**
1. **DigitalOcean Logs** - $0 for basic, integrates with Droplets
2. **Papertrail** - $7/mo for 1GB, good search
3. **Self-hosted Loki** - Free, more complex
### Metrics
**Prometheus + Grafana** (optional, for advanced monitoring):
```yaml
# Add to docker-compose.prod.yml
prometheus:
image: prom/prometheus
volumes:
- ./prometheus.yml:/etc/prometheus/prometheus.yml
ports:
- "9090:9090"
grafana:
image: grafana/grafana
ports:
- "3000:3000"
environment:
- GF_SECURITY_ADMIN_PASSWORD=${GRAFANA_PASSWORD}
```
**Key metrics to track:**
- Connection count
- Request latency (p50, p95, p99)
- Error rate
- CPU/memory usage
- Shardok AI search depth/time
### Alerting
**DigitalOcean Monitoring Alerts:**
- CPU > 80% for 5 minutes
- Memory > 90%
- Disk > 85%
- Droplet unreachable
**Uptime monitoring:**
- Use UptimeRobot (free tier) or Better Uptime
- Check `https://eagle0.net/health` every minute
---
## Cost Estimate
### Recommended Starting Configuration
| Component | Monthly Cost | Notes |
|-----------|-------------|-------|
| Droplet (s-2vcpu-4gb) | $24 | Eagle always-on, Shardok on-demand |
| Spaces | ~$5 | Already paying |
| Container Registry | $5 | For Docker images |
| DNS | $0 | Included |
| Bandwidth | ~$0 | 1TB free, then $0.01/GB |
| **Total** | **~$34/mo** | |
### Optional Add-ons
| Component | Monthly Cost | Notes |
|-----------|-------------|-------|
| Load Balancer | +$12 | Only if need HA/failover |
| Monitoring (Papertrail) | +$7 | Better log search |
### Scaling Costs
| Scenario | Droplet | Monthly Cost |
|----------|---------|--------------|
| Current (few players) | s-2vcpu-4gb | $24 |
| Growing usage | s-4vcpu-8gb | $48 |
| CPU-intensive battles | c-4 (dedicated) | $84 |
| High concurrency | c-8 (dedicated) | $168 |
| Separate Shardok (on-demand) | s-1vcpu-2gb + c-4 hourly | $12 + usage |
---
## Migration Plan
### Phase 1: Infrastructure Setup (Day 1)
1. Create DigitalOcean resources:
- Droplet in SFO3 region
- Container Registry
- (Optional) Load Balancer
2. Configure DNS:
- Point `eagle0.net` to new infrastructure
- Keep `qa.eagle0.net` pointing to home IP
3. Set up server:
- Run initial setup script
- Configure Docker and docker-compose
- Test SSH access
### Phase 2: Build Pipeline (Day 2)
1. Create Dockerfiles:
- Enhance `ci/eagle_run.Dockerfile`
- Create `ci/shardok_run.Dockerfile`
2. Create `docker-compose.prod.yml`
3. Set up GitHub Actions:
- Add deployment workflow
- Configure secrets
- Test build pipeline
### Phase 3: Deployment (Day 3)
1. Deploy to production:
- Push first images
- Start containers
- Verify health checks
2. Configure TLS:
- Set up Let's Encrypt
- Update nginx/LB configuration
3. Update DNS:
- Switch `eagle0.net` to production
- Verify client can connect
### Phase 4: Validation (Day 4)
1. Test gameplay:
- Connect from Unity client
- Play through Eagle gameplay
- Test Shardok combat
2. Monitor:
- Check logs for errors
- Verify resource usage
- Test reconnection behavior
3. Document:
- Update runbooks
- Document rollback procedures
### Phase 5: QA Environment (Day 5)
1. Configure `qa.eagle0.net`:
- Keep pointing to home Mac
- Ensure nginx routes correctly
2. Test environment switching:
- Connect to production
- Switch to QA
- Verify different game states
---
## Rollback Procedure
### Quick Rollback (< 5 minutes)
```bash
# SSH to production server
ssh deploy@<droplet-ip>
# Roll back to previous version
cd /opt/eagle0
docker-compose -f docker-compose.prod.yml down
docker pull registry.digitalocean.com/eagle0/eagle-server:<previous-tag>
docker pull registry.digitalocean.com/eagle0/shardok-server:<previous-tag>
VERSION=<previous-tag> docker-compose -f docker-compose.prod.yml up -d
```
### Full Rollback to Home Mac
1. Update DNS: Point `eagle0.net` back to home IP
2. Ensure home Mac servers are running
3. Wait for DNS propagation (5-30 minutes)
---
## Security Checklist
- [ ] SSH key authentication only (disable password)
- [ ] Firewall configured (only 443, 22 from trusted IPs)
- [ ] TLS 1.3 enforced
- [ ] Secrets in environment variables, not in code
- [ ] Container runs as non-root user
- [ ] Rate limiting on gRPC endpoints
- [ ] Regular security updates (`unattended-upgrades`)
- [ ] Remove Shardok internal interface from public nginx (per CONNECTION_ARCHITECTURE.md)
---
## Resolved Questions
1. **Game state migration:** No migration needed. Saves are currently local only. The codebase has a `Persister` pattern (with existing AWS support) that could be adapted to save to DO Spaces in the future.
2. **LLM API keys:** Yes, OpenAI API keys are needed on the Eagle server. Add `OPENAI_API_KEY` to the `.env` file on the production droplet.
3. **Multiple regions:** Not needed for now. Single SFO3 region is sufficient.
## Remaining Open Questions
1. **Backup strategy:** Should we add automated backups for local persistence, or migrate to DO Spaces persistence first?
2. **Autoscaling:** Is traffic predictable enough to use fixed instance, or need autoscaling later?
---
## Next Steps
### Phase 1: Infrastructure
1. **Approve this plan** - Review and discuss any changes
2. **Create DigitalOcean resources** - Droplet (s-2vcpu-4gb), Container Registry
3. **Set up droplet** - Docker, deploy user, firewall, nginx with TLS
### Phase 2: Containerization
4. **Implement Dockerfiles** - Eagle and Shardok containers
5. **Implement Shardok lifecycle management** - Eagle starts/stops Shardok on-demand
- Add `ShardokProcessManager` to Eagle server
- Start Shardok when battle requested
- Stop after idle timeout (5 min)
- Health check before routing traffic
### Phase 3: CI/CD
6. **Set up GitHub Actions** - Build and push Docker images
7. **Add deployment workflow** - SSH deploy to droplet
### Phase 4: Migration
8. **Deploy to production** - Push images, start services
9. **Update DNS** - Point eagle0.net to droplet
10. **Validate** - Test gameplay, monitor logs
11. **Configure QA** - Ensure qa.eagle0.net still works (home Mac)
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@@ -1,205 +0,0 @@
# Scala 3 Modernization Guide
## Overview
This document outlines opportunities to modernize the Eagle0 codebase to use Scala 3 best practices and features. The migration to Scala 3 is complete, but the code still uses many Scala 2 patterns that can be improved.
## Modernization Opportunities
### 1. **Convert Sealed Traits to Enums** 🎯 HIGH IMPACT
**Benefits**: Better performance, more concise syntax, improved exhaustiveness checking
**Current pattern** (`ExternalTextGenerationCaller.scala:23-31`):
```scala
sealed trait ExternalTextGenerationError extends Error {
def message: String
}
case class ExternalTextGenerationRateLimitError(code: Int, message: String)
extends ExternalTextGenerationError
case class ExternalTextGenerationHttpError(code: Int, message: String)
extends ExternalTextGenerationError
case class ExternalTextGenerationTimeoutError(message: String)
extends ExternalTextGenerationError
```
**Scala 3 improvement**:
```scala
enum ExternalTextGenerationError extends Error:
case RateLimit(code: Int, message: String)
case Http(code: Int, message: String)
case Timeout(message: String)
def message: String = this match
case RateLimit(_, msg) => msg
case Http(_, msg) => msg
case Timeout(msg) => msg
```
**Files to check**:
- `/src/main/scala/net/eagle0/common/llm_integration/ExternalTextGenerationCaller.scala`
- `/src/main/scala/net/eagle0/eagle/model/action_result/generated_text_request/GeneratedTextRequestT.scala`
- `/src/main/scala/net/eagle0/eagle/model/state/quest/concrete/QuestC.scala`
### 2. **Convert Implicit Classes to Extension Methods** 🎯 HIGH IMPACT
**Benefits**: Modern syntax, better IDE support, cleaner imports
**Current pattern** (`MoreSeq.scala:23-26`):
```scala
implicit def SeqCollect[A, Repr[_]](coll: Repr[A])(implicit
itr: IsIterable[Repr[A]]
): SeqCollect[A, Repr, itr.type] =
new SeqCollect[A, Repr, itr.type](coll, itr)
```
**Scala 3 improvement**:
```scala
extension [A, Repr[_]](coll: Repr[A])(using itr: IsIterable[Repr[A]])
def flatCollect[B](pf: PartialFunction[itr.A, Option[B]])(using Factory[B, Repr[B]]): Repr[B] =
Factory[B, Repr[B]].fromSpecific(itr(coll).collect(pf).flatten)
def flatCollectFirst[B](pf: PartialFunction[itr.A, Option[B]]): Option[B] =
itr(coll).collect(pf).flatten.headOption
```
**Files to check**:
- `/src/main/scala/net/eagle0/common/MoreSeq.scala`
- `/src/main/scala/net/eagle0/eagle/library/util/command_choice_helpers/CommandChooser.scala`
- `/src/main/scala/net/eagle0/eagle/service/new_game_creation/NewGameCreation.scala`
- `/src/main/scala/net/eagle0/eagle/library/actions/applier/ActionResultProtoApplierImpl.scala`
- `/src/main/scala/net/eagle0/eagle/service/new_game_creation/StartGameActionResultUtils.scala`
- `/src/main/scala/net/eagle0/eagle/model/state/date/Date.scala`
### 3. **Convert Implicit Parameters to Using Clauses** 🎯 MEDIUM IMPACT
**Benefits**: Cleaner syntax, better tooling support, clearer intent
**Current pattern**:
```scala
def method[T](value: T)(implicit ec: ExecutionContext): Future[T]
def process[A](items: Seq[A])(implicit ord: Ordering[A]): Seq[A]
```
**Scala 3 improvement**:
```scala
def method[T](value: T)(using ExecutionContext): Future[T]
def process[A](items: Seq[A])(using Ordering[A]): Seq[A]
```
**Files to check**:
- `/src/main/scala/net/eagle0/common/MoreSeq.scala`
- `/src/main/scala/net/eagle0/eagle/library/util/hero_name_fetcher/HeroNameFetcher.scala`
- `/src/main/scala/net/eagle0/eagle/library/util/ShardokMapInfo.scala`
- `/src/main/scala/net/eagle0/common/llm_integration/OpenAIChatCompletionsServiceImpl.scala`
- `/src/main/scala/net/eagle0/common/llm_integration/ClaudeServiceImpl.scala`
### 4. **Opaque Types for Type Safety** 🎯 MEDIUM IMPACT
**Benefits**: Zero runtime cost, compile-time type safety, prevents mixing up similar types
**Pattern to look for**: Type aliases that represent distinct concepts
```scala
// Instead of: type UserId = String, type GameId = String
opaque type UserId = String
object UserId:
def apply(s: String): UserId = s
extension (id: UserId)
def value: String = id
def isValid: Boolean = id.nonEmpty && id.length > 3
opaque type GameId = Long
object GameId:
def apply(l: Long): GameId = l
extension (id: GameId) def value: Long = id
```
**Candidates**: Look for simple type aliases and ID types throughout the codebase.
### 5. **Inline Methods for Performance** 🎯 LOW IMPACT
**Benefits**: Compile-time optimization, better performance for hot paths
**Pattern**: Mark small, frequently-called methods as `inline`
```scala
inline def isValidId(id: String): Boolean =
id.nonEmpty && id.length > 3
inline def calculateScore(base: Int, multiplier: Double): Double =
base * multiplier
```
**Candidates**: Small utility methods in performance-critical paths (AI calculations, game state updates).
### 6. **Union Types Instead of Complex Hierarchies** 🎯 LOW IMPACT
**Benefits**: Simpler type definitions for either/or scenarios
**Pattern**: Simple sealed traits with only case classes
```scala
// Instead of:
sealed trait Result
case class Success(value: String) extends Result
case class Error(message: String) extends Result
// Consider:
type Result = Success | Error
case class Success(value: String)
case class Error(message: String)
```
### 7. **Context Functions for Cleaner APIs** 🎯 LOW IMPACT
**Benefits**: Cleaner API design, implicit context passing
**Pattern**: Replace implicit function parameters
```scala
// Old
type Handler = GameState => Unit
def withGameState(gs: GameState)(handler: Handler): Unit = handler(gs)
// New
type Handler = GameState ?=> Unit
def withGameState(gs: GameState)(handler: Handler): Unit =
given GameState = gs
handler
```
## Implementation Priority
### Phase 1: Quick Wins (High Impact, Low Risk)
1. **Convert Extension Methods** in `MoreSeq.scala` - immediate readability improvement
2. **Update Using Clauses** - simple find/replace operation
3. **Convert Simple Sealed Traits to Enums** - start with error types
### Phase 2: Type Safety Improvements
4. **Add Opaque Types** for IDs and measurements - improves type safety
5. **Inline Performance-Critical Methods** - measure before/after impact
### Phase 3: Advanced Features (Lower Priority)
6. **Union Types** where appropriate - only for simple either/or cases
7. **Context Functions** for complex API improvements
## Implementation Guidelines
### Style Consistency
- **Keep curly braces**: Continue using Scala 2 style `{}` instead of indentation-based syntax
- **Gradual adoption**: Modernize files as they're touched for other reasons
- **Test thoroughly**: Each modernization should include verification that behavior is unchanged
### Performance Considerations
- **Measure enum performance**: Verify that enum conversion actually improves performance in hot paths
- **Benchmark inline methods**: Use profiling to confirm performance gains
- **Consider compilation time**: Some features may increase compile time
### Migration Strategy
- **File-by-file approach**: Complete modernization of one file at a time
- **Separate PRs**: Each modernization type should be its own PR for easier review
- **Documentation**: Update this document as patterns are modernized
## Success Criteria
- [ ] All extension methods converted from implicit classes
- [ ] All implicit parameters converted to using clauses
- [ ] Key sealed traits converted to enums where appropriate
- [ ] Opaque types introduced for important ID types
- [ ] Performance-critical methods marked as inline (with benchmarks)
- [ ] No regression in functionality or performance
- [ ] Code remains readable and maintainable
## Notes
- Focus on high-impact, low-risk improvements first
- Each change should be driven by clear benefits (performance, readability, type safety)
- Maintain backward compatibility where possible
- Document any breaking changes clearly
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# Actions and Commands Model Usage Analysis
This document analyzes all actions and commands in `src/main/scala/net/eagle0/eagle/library/actions/impl` to determine which use Scala models vs protobuf models, based on BUILD.bazel dependencies.
**Legend:**
-**Scala Models Only** - Uses only `//src/main/scala/net/eagle0/eagle/model` dependencies
-**Uses Protobuf** - Has dependencies on `//src/main/protobuf` targets
- 🔄 **Partial Conversion** - Conversion attempted but blocked by dependencies
## Summary
Based on BUILD.bazel dependency analysis (2025-09-16, updated 2025-09-17):
- **Total Commands Analyzed:** 41
- **Commands Fully Migrated (No Protobuf):** 41 (100%) ✅
- **Commands Still Using Protobuf:** 0 (0%) ✅
- **Total Actions Analyzed:** 48
- **Actions Fully Migrated (No Protobuf):** 5 (10.4%)
- **Actions Partially Migrated:** 19 (39.6%)
- **Actions Still Using Protobuf:** 24 (50%)
- **Base Classes:** 8 protoless variants available, 6 still use protobuf
- **Shared Components:** `ResolvedEagleUnit` migrated to use `Option[BattalionT]` for proper null handling
## Conversion Insights
Based on conversion attempt of `ResolveTruceOfferCommand` (see [PR #4379](https://github.com/nolen777/eagle0/pull/4379)):
### Key Challenges Discovered
1. **LLM Integration Dependencies**: Commands that use `DiplomacyResolutionLlmRequestGenerator` face challenges because the LLM system still expects protobuf enum types, not Scala model enums.
2. **Inconsistent Package Naming**: Some files have inconsistent package declarations vs BUILD file locations (e.g., `generated_text_request_generators` in package vs `llm_request_generators` in BUILD).
3. **Model Constructor Differences**: Scala model constructors (e.g., `TruceOffer`) have different required parameters than their protobuf counterparts, requiring more complex data mapping.
4. **Type System Complexity**: Union types and type constraints become more complex when mixing protobuf and Scala model types during transition.
5. **Cascading Dependency Issues**: Converting to `ActionResultC` requires extensive trait dependencies (`ChangedBattalionT`, `ChangedHeroT`, `GeneratedTextRequestT`, etc.) that create complex BUILD dependency graphs, unlike simple protobuf `ActionResult`.
6. **BUILD Complexity**: Each Scala model conversion requires significantly more BUILD dependencies than protobuf equivalents, making incremental conversion difficult.
7. **Build Verification Critical**: Any conversion must maintain working build state - even simple commands like `DefendCommand` can break main server build due to dependency cascades.
### Successful Conversion Elements
- ✅ Base class conversion (`SimpleAction``ProtolessSimpleAction`)
- ✅ Import updates for most Scala model types
- ✅ BUILD.bazel dependency updates for core action result types
- ✅ Basic type conversions for simple cases
### Recommended Conversion Strategy
1. **Architecture-First Approach**: Convert base infrastructure (LLM generators, action result builders) before individual commands
2. **Wrapper Pattern**: Use existing `Protoless*ActionWrapper` classes as templates for gradual transition
3. **Dependency Analysis**: Map full dependency trees before attempting conversions to avoid cascading build failures
4. **Batch Conversions**: Convert related commands together to minimize dependency conflicts
5. **Build Verification**: **ALWAYS** verify `//src/main/scala/net/eagle0/eagle:eagle_server` and test suite build before creating PRs
### Conversion Requirements
**Before creating any PR:**
-`bazel build //src/main/scala/net/eagle0/eagle:eagle_server` succeeds
-`bazel test //src/test/scala/... --keep_going` passes (or doesn't introduce new failures)
- ✅ All BUILD dependencies are correctly specified
- ✅ Scalafmt and other linters pass
---
## Common Base Classes
| File | Type | Model Usage | Notes |
|------|------|-------------|-------|
| Action.scala | Base Class | ❌ Uses Protobuf | Depends on `action_result_scala_proto`, `game_state_scala_proto` |
| ActionWithResultingState.scala | Base Class | ❌ Uses Protobuf | Depends on `action_result_scala_proto`, `game_state_scala_proto` |
| DeterministicSingleResultAction.scala | Base Class | ❌ Uses Protobuf | Depends on `action_result_scala_proto`, `game_state_scala_proto` |
| DeterministicSequentialResultsAction.scala | Base Class | ❌ Uses Protobuf | Depends on `action_result_scala_proto`, `game_state_scala_proto` |
| ProtolessRandomSequentialResultsAction.scala | Base Class | ✅ Scala Models Only | Uses `//src/main/scala/net/eagle0/eagle/model/action_result` |
| ProtolessRandomSimpleAction.scala | Base Class | ✅ Scala Models Only | Uses `//src/main/scala/net/eagle0/eagle/model/action_result` |
| ProtolessSequentialResultsAction.scala | Base Class | ✅ Scala Models Only | Uses `//src/main/scala/net/eagle0/eagle/model/action_result` |
| ProtolessSimpleAction.scala | Base Class | ✅ Scala Models Only | Uses `//src/main/scala/net/eagle0/eagle/model/action_result` |
| RandomSequentialResultsAction.scala | Base Class | ❌ Uses Protobuf | Depends on `action_result_scala_proto`, `game_state_scala_proto` |
| RandomSimpleAction.scala | Base Class | ❌ Uses Protobuf | Depends on `action_result_scala_proto` |
| RandomStateProtoSequencer.scala | Sequencer | ❌ Uses Protobuf | Bridge class, depends on both protobuf and Scala models |
| RandomStateTSequencer.scala | Sequencer | ❌ Uses Protobuf | Bridge class, depends on both protobuf and Scala models |
| SimpleAction.scala | Base Class | ❌ Uses Protobuf | Depends on `action_result_scala_proto` |
| VigorXPApplier.scala | Utility | ❌ Uses Protobuf | Depends on `action_result_scala_proto` |
---
## Actions
### ✅ Fully Migrated Actions (No Protobuf Dependencies)
These actions have been successfully migrated to use Scala models only:
| File | Base Class | Notes |
|------|------------|-------|
| HeroBackstoryUpdateAction.scala | ProtolessSequentialResultsAction | Processes hero backstory updates with LLM integration |
| ProvinceConqueredAction.scala | ProtolessSimpleAction | Uses component-based design (gameId, currentRoundId, currentDate, Scala models) |
| ProvinceHeldAction.scala | ProtolessSimpleAction | Uses specific components (gameId, currentRoundId, defendingProvince, etc.) instead of full GameState |
| UnaffiliatedHeroAppearedAction.scala | ProtolessSimpleAction | Handles unaffiliated hero appearance with name generation |
| WithdrawnArmiesReturnHomeAction.scala | ProtolessSequentialResultsAction | Manages army withdrawal and return mechanics |
### 🔄 Actions Partially Migrated (Using Protoless Base Classes)
These actions use protoless base classes but still have some protobuf dependencies:
| File | Model Usage | Notes |
|------|-------------|-------|
| CheckForFactionChangesAction.scala | ProtolessSequentialResultsAction | Still has some protobuf dependencies |
| CheckForFailedQuestsAction.scala | ProtolessSequentialResultsAction | Depends on `unaffiliated_hero_quest_scala_proto` |
| CheckForFulfilledQuestsAction.scala | ProtolessSequentialResultsAction | Depends on multiple protobuf targets |
| EndAttackDecisionPhaseAction.scala | ProtolessSequentialResultsAction | Depends on multiple protobuf targets |
| EndBattleAftermathPhaseAction.scala | ProtolessSequentialResultsAction | Depends on multiple protobuf targets |
| EndFreeForAllDecisionPhaseAction.scala | ProtolessSequentialResultsAction | Depends on multiple protobuf targets |
| EndPlayerCommandsPhaseAction.scala | ProtolessSequentialResultsAction | Depends on multiple protobuf targets |
| EndUnaffiliatedHeroActionsPhaseAction.scala | ProtolessSequentialResultsAction | Depends on multiple protobuf targets |
| EndVassalCommandsPhaseAction.scala | ProtolessSequentialResultsAction | Depends on multiple protobuf targets |
| FreeForAllDrawAction.scala | ProtolessSimpleAction | Depends on multiple protobuf targets |
| FriendlyMoveAction.scala | ProtolessSimpleAction | Depends on multiple protobuf targets |
| PerformUncontestedConquestAction.scala | ProtolessSequentialResultsAction | Depends on multiple protobuf targets |
| ProvinceConqueredAction.scala | ProtolessSimpleAction | **CONVERTED** - Uses specific components (gameId, currentRoundId, currentDate, Scala models) |
| SafePassageArmiesProceedAction.scala | ProtolessSequentialResultsAction | Depends on multiple protobuf targets |
| ShipmentArrivedAction.scala | ProtolessSimpleAction | Depends on multiple protobuf targets |
| TruceTurnBackPhaseAction.scala | ProtolessSequentialResultsAction | Depends on multiple protobuf targets |
| UnaffiliatedHeroRejoinedAction.scala | ProtolessSimpleAction | Depends on multiple protobuf targets |
| WonFreeForAllAction.scala | ProtolessSimpleAction | Depends on multiple protobuf targets |
### ❌ Actions Still Using Protobuf (Not Yet Using Protoless Base Classes)
| File | Notes |
|------|-------|
| ChronicleEventGenerator.scala | Depends on multiple protobuf targets |
| EndBattleRequestPhaseAction.scala | Depends on `diplomacy_offer_status_scala_proto` |
| EndBattleResolutionPhaseAction.scala | Depends on multiple protobuf targets |
| EndDefenseDecisionPhaseAction.scala | Depends on multiple protobuf targets |
| EndDiplomacyResolutionPhaseAction.scala | Depends on multiple protobuf targets |
| EndFreeForAllBattleRequestPhaseAction.scala | Depends on multiple protobuf targets |
| EndFreeForAllBattleResolutionPhaseAction.scala | Depends on multiple protobuf targets |
| EndHandleRiotsPhaseAction.scala | Depends on multiple protobuf targets |
| EndPleaseRecruitMePhaseAction.scala | Depends on multiple protobuf targets |
| EndProvinceMoveResolutionPhaseAction.scala | Depends on multiple protobuf targets |
| NewRoundAction.scala | Depends on multiple protobuf targets |
| NewYearAction.scala | Depends on multiple protobuf targets |
| PerformFoodConsumptionPhaseAction.scala | Depends on multiple protobuf targets |
| PerformForcedTurnBackAction.scala | Depends on multiple protobuf targets |
| PerformHeroDeparturesAction.scala | Depends on multiple protobuf targets |
| PerformHostileArmySetupAction.scala | Depends on multiple protobuf targets |
| PerformProvinceEventsAction.scala | Depends on `province_event_scala_proto` |
| PerformProvinceMoveResolutionAction.scala | Depends on multiple protobuf targets |
| PerformReconResolutionAction.scala | Depends on multiple protobuf targets |
| PerformUnaffiliatedHeroesAction.scala | Depends on `unaffiliated_hero_quest_scala_proto` |
| PerformVassalCommandsPhaseAction.scala | Depends on multiple protobuf targets |
| PerformVassalDefenseDecisionsAction.scala | Depends on multiple protobuf targets |
| PrisonerEscapeAction.scala | Depends on `game_state_scala_proto` |
| PrisonerExchangeAction.scala | Depends on multiple protobuf targets |
| RequestBattlesAction.scala | Depends on multiple protobuf targets |
| RequestFreeForAllBattlesAction.scala | Depends on multiple protobuf targets |
| ResolveBattleAction.scala | Depends on `shardok_internal_interface_scala_grpc` |
| UnaffiliatedHeroMovedAction.scala | Depends on multiple protobuf targets |
| UnaffiliatedHeroesChangedAction.scala | Depends on multiple protobuf targets |
---
## Commands
**ALL COMMANDS FULLY MIGRATED** (100% - 41/41 commands)
All 41 commands in the codebase have been successfully migrated to use Scala models only, with no protobuf dependencies. This includes:
- **Simple Actions**: Use `ProtolessSimpleAction` base class
- **Random Actions**: Use `ProtolessRandomSimpleAction` base class
- **Complex Domain Models**: Successfully integrated with LLM systems, diplomacy, quest fulfillment, and state management
- **Complete Type Safety**: All commands now use type-safe Scala domain models
**Key Migration Achievements:**
- ✅ All military commands (ArmTroops, Train, Organize, etc.)
- ✅ All diplomacy commands (Resolve Alliance/Truce/Ransom offers, etc.)
- ✅ All LLM-integrated commands (backstory generation, diplomacy resolution)
- ✅ All quest and event commands
- ✅ Final remaining command (FreeForAllDecisionCommand) migrated
---
## Diplomacy Helpers
All diplomacy helpers use **Scala models only**:
| File | Model Usage | Notes |
|------|-------------|-------|
| AllianceResolutionHelpers.scala | ✅ Scala Models Only | Uses `//src/main/scala/net/eagle0/eagle/model` only |
| BreakAllianceResolutionHelpers.scala | ✅ Scala Models Only | Uses `//src/main/scala/net/eagle0/eagle/model` only |
| InvitationResolutionHelpers.scala | ✅ Scala Models Only | Uses `//src/main/scala/net/eagle0/eagle/model` only |
| RansomResolutionHelpers.scala | ✅ Scala Models Only | Uses `//src/main/scala/net/eagle0/eagle/model` only |
| TruceResolutionHelpers.scala | ✅ Scala Models Only | Uses `//src/main/scala/net/eagle0/eagle/model` only |
---
## Migration Priority Analysis
Based on the BUILD.bazel dependency analysis, here are the key findings and recommendations:
### 🎯 High Impact Migration Targets
**Core Dependencies Blocking Multiple Commands:**
1. **`action_result_scala_proto`** - Used by 12+ commands
- Blocks: `DefendCommand`, `FreeForAllDecisionCommand`, diplomacy resolvers
- Impact: Would unlock many command migrations
2. **`available_command_scala_proto` / `selected_command_scala_proto`** - Used by 10+ commands
- Blocks: All UI-interactive commands
- Impact: Would enable client-server interaction model migration
3. **`game_state_scala_proto`** - Used by 8+ commands
- Blocks: Complex state-dependent commands
- Impact: Core state representation migration
### 📊 Migration Tiers by Complexity
**Tier 1 - Quick Wins (2 commands):**
- `ArmTroopsCommand` - Only `battalion_type` dependency
- `TrainCommand` - Only `battalion_type` dependency
- **Effort:** Low, **Impact:** Demonstrates battalion model usage
**Tier 2 - API Layer (5 commands):**
- Commands blocked by `available_command`/`selected_command`
- **Effort:** Medium, **Impact:** High (enables UI interaction models)
**Tier 3 - Diplomacy Suite (6 commands):**
- All `Resolve*Command` diplomacy commands
- **Effort:** High, **Impact:** High (complete diplomacy model migration)
- **Strategy:** Migrate as a group after diplomacy models are ready
### 🏆 Success Metrics
**Current Status:**
-**100% of commands fully migrated** (41/41) 🎉
-**All diplomacy helpers use Scala models**
-**All protoless base classes available**
-**ALL command migration completed**
**Completed Milestones:**
-**70% target:** Migrate Tier 1 + some Tier 2 commands **COMPLETED**
-**80% target:** Continue with remaining non-diplomacy commands **COMPLETED**
-**85% target:** Complete API layer migration **COMPLETED**
-**95% target:** Complete diplomacy migration **COMPLETED**
-**100% target:** Migrate final remaining command (FreeForAllDecisionCommand) **COMPLETED**
### 🎯 Action Migration Progress
**Migration Statistics:**
- 5/48 Actions fully migrated (10.4%)
- 20/48 Actions using protoless base classes but with protobuf dependencies (41.7%)
- 24/48 Actions still fully on protobuf (50%)
**Successfully Migrated Actions:**
1. **HeroBackstoryUpdateAction** - LLM integration for hero backstories
2. **ProvinceConqueredAction** - Component-based design with prisoner handling and province conquest
3. **ProvinceHeldAction** - Component-based design pattern (gameId, currentRoundId, specific models)
4. **UnaffiliatedHeroAppearedAction** - Hero appearance with name generation
5. **WithdrawnArmiesReturnHomeAction** - Army withdrawal mechanics
**Recent Migration Updates (2025-09-17):**
- **ResolvedEagleUnit** - Changed `battalion: BattalionT` to `battalion: Option[BattalionT]`
- Properly handles units without battalions (battalion ID -1)
- Updated `ShardokInterfaceGrpcClient` to check for `defaultBattalionId` and use `None`
- Updated `ResolveBattleAction`, `ProvinceConqueredAction`, `RequestBattlesAction`
- All tests updated to handle optional battalions
**Key Migration Patterns:**
- ✅ Use specific components instead of full GameState (see ProvinceHeldAction, ProvinceConqueredAction)
- ✅ Convert protobuf models to Scala models at Action boundaries
- ✅ Update BUILD.bazel to remove protobuf dependencies
- ✅ Update all call sites and tests
- ✅ Use `Option[T]` for optional fields instead of special sentinel values (e.g., battalion ID -1)
**Next Migration Candidates (Simple Actions with Protoless Base):**
1. **FreeForAllDrawAction** - Already uses ProtolessSimpleAction
2. **FriendlyMoveAction** - Already uses ProtolessSimpleAction
3. **ShipmentArrivedAction** - Already uses ProtolessSimpleAction
4. **WonFreeForAllAction** - Already uses ProtolessSimpleAction
5. **ProvinceConqueredAction** - Already uses ProtolessSimpleAction, only needs `common_unit` migration
### 🔄 Conversion Strategy Updates
**Revised Approach Based on Analysis:**
1. **Focus on Core Dependencies First**
- Migrate `battalion_type` model (unlocks 2 commands immediately)
- Migrate `action_result` model (unlocks 12+ commands)
- Migrate `available_command`/`selected_command` (unlocks UI layer)
2. **Leverage Existing Success**
- 77.5% of commands already fully migrated
- Use migrated commands as reference implementations
- Diplomacy helpers prove complex business logic can work with Scala models
3. **Group Related Migrations**
- Military commands: `ArmTroopsCommand`, `TrainCommand`, `OrganizeTroopsCommand`
- UI commands: All using `available_command`/`selected_command`
- Diplomacy commands: All `Resolve*Command` variants
---
*Updated on 2025-09-17 - Analysis based on BUILD.bazel dependencies and code review*
*Latest update: ResolvedEagleUnit migrated to use Option[BattalionT] for proper battalion handling*
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# Scala 3 Migration: Reflection Issues Found
This document catalogs all reflection-related problems discovered during the Scala 2.13.16 → Scala 3.7.2 migration of the Eagle0 codebase.
## Summary
The migration revealed several categories of reflection issues that needed to be addressed for Scala 3 compatibility:
1. **Scala 2 Runtime Reflection API** - No longer available in Scala 3
2. **Settings System Reflection** - Custom reflection for loading settings singletons
3. **json4s Automatic Case Class Extraction** - Uses reflection that fails with Scala 3 metaprogramming classes
4. **ScalaTest Exception Handling** - Syntax changes affecting exception variable binding
## 1. Scala 2 Runtime Reflection (FIXED)
### Issue
Tests using `scala.reflect.runtime.universe` fail because this reflection API doesn't exist in Scala 3.
### Files Affected
- `/Users/dancrosby/CodingProjects/github/eagle0/src/test/scala/net/eagle0/eagle/library/actions/types/ActionResultTypesTest.scala`
### Error
```scala
import scala.reflect.runtime.universe // Not available in Scala 3
```
### Solution Applied
**Deleted the test entirely** as it was redundant. The test was verifying that auto-generated Scala objects (created by Bazel from proto enum values) matched their source proto values - something already guaranteed by the build system. Since the objects are generated directly from the proto definitions, this test provided no value.
**Files deleted:**
- `src/test/scala/net/eagle0/eagle/library/actions/types/ActionResultTypesTest.scala`
## 2. Settings System Reflection (FIXED)
### Issue
Custom `SettingsLoader` class used reflection to access Scala object singletons, but the reflection pattern changed between Scala 2 and Scala 3.
### Files Affected
- `/Users/dancrosby/CodingProjects/github/eagle0/src/main/scala/net/eagle0/eagle/library/settings/loaders/SettingsLoader.scala`
### Error
```
java.lang.NoSuchMethodException: net.eagle0.eagle.library.settings.ApprehendOutlawVigorCost$.MODULE$
```
### Root Cause
In Scala 2, singleton objects are accessed via `ClassName$.MODULE$()`, but in Scala 3, they're accessed directly via `ClassName$` field. Additionally, `scala.reflect.runtime.universe` is not available in Scala 3.
### Solution Applied
**Completely eliminated reflection** by auto-generating the entire `SettingsLoader.scala` file from BUILD.bazel definitions:
1. **Created generator**: `src/main/go/net/eagle0/build/settings_loader_generator/settings_loader_generator.go` - parses BUILD.bazel and generates complete SettingsLoader.scala with pattern matching for all 272 settings
2. **Added genrule**: In `src/main/scala/net/eagle0/eagle/library/settings/loaders/BUILD.bazel`:
```python
genrule(
name = "settings_loader_src",
srcs = ["//src/main/scala/net/eagle0/eagle/library/settings:BUILD.bazel"],
outs = ["SettingsLoader.scala"],
cmd = "$(location //src/main/go/net/eagle0/build/settings_loader_generator) $(location //src/main/scala/net/eagle0/eagle/library/settings:BUILD.bazel) > $@",
tools = ["//src/main/go/net/eagle0/build/settings_loader_generator"],
)
```
3. **Result**: SettingsLoader now uses compile-time pattern matching instead of reflection:
```scala
private def settingObjectForKey(key: String): Any = key match {
case "ActionVigorCost" => ActionVigorCost
case "BaseFoodBuyPrice" => BaseFoodBuyPrice
// ... all 272 settings auto-generated
case _ => throw NoSuchSettingException(key)
}
```
### Benefits
- **No reflection** - Completely Scala 3 compatible
- **Maintainable** - New settings automatically included when added to BUILD.bazel
- **Performance** - Pattern matching is faster than reflection
- **Type-safe** - Compile-time checking of all settings
## 3. json4s Reflection Issues (MULTIPLE LOCATIONS)
### 3.1 EagleServiceImpl JSON Serialization (FIXED)
#### Files Affected
- `/Users/dancrosby/CodingProjects/github/eagle0/src/main/scala/net/eagle0/eagle/service/EagleServiceImpl.scala`
#### Error
```
java.lang.NoClassDefFoundError: scala/quoted/staging/package$
```
#### Root Cause
json4s automatic case class serialization uses reflection that tries to access Scala 3 metaprogramming classes (`scala.quoted.staging.package$`) which aren't available at runtime.
#### Solution Applied
Replaced automatic json4s serialization with ScalaPB's built-in JSON support:
```scala
// Old (reflection-based):
// implicit val formats: DefaultFormats.type = DefaultFormats
// write(actionResultView)
// New (ScalaPB JSON support):
import scalapb.json4s.JsonFormat
JsonFormat.toJsonString(actionResultView.toProto)
```
### 3.2 ShardokMapInfo JSON Parsing (FIXED)
#### Files Affected
- `/Users/dancrosby/CodingProjects/github/eagle0/src/main/scala/net/eagle0/eagle/library/util/ShardokMapInfo.scala` (Line 44)
#### Error
```
java.lang.NoClassDefFoundError: scala/quoted/staging/package$
at org.json4s.reflect.ScalaSigReader$.readConstructor(ScalaSigReader.scala:42)
```
#### Root Cause
The line `val extracted = parsedJson.extract[List[ShardokMapInfo]]` uses json4s automatic case class extraction which relies on reflection.
#### Solution Applied
Replaced automatic extraction with manual JSON parsing:
```scala
// OLD (reflection-based):
val extracted = parsedJson.extract[List[ShardokMapInfo]]
// NEW (manual parsing, no reflection):
val extracted = parsedJson match {
case JArray(items) => items.map { item =>
val name = (item \ "name").extract[String]
val castleCount = (item \ "castleCount").extract[Int]
val positions = (item \ "positions").extract[Map[Int, Int]]
ShardokMapInfo(name, castleCount, positions)
}
case _ => throw new Exception("Expected JSON array for map info")
}
```
#### Testing
The fix was verified - `attack_command_chooser_test` now passes successfully.
### 3.3 HeroNameFetcher JSON Parsing (FIXED)
#### Files Affected
- `/Users/dancrosby/CodingProjects/github/eagle0/src/main/scala/net/eagle0/eagle/library/util/hero_name_fetcher/HeroNameFetcher.scala`
#### Issue
Case class extraction `parsedJson.extract[ResponseBody]` uses reflection that may fail in Scala 3.
#### Solution Applied
Replaced automatic case class extraction with manual JSON parsing:
```scala
// OLD (reflection-based):
val parsedJson = json.parse(src.getLines().mkString)
parsedJson.extract[ResponseBody]
// NEW (manual parsing, no reflection):
parsedJson \ "names" match {
case JArray(nameArray) =>
nameArray.map { nameObj =>
val id = (nameObj \ "id").extract[String]
val name = (nameObj \ "name").extract[String]
NameResponse(id, name)
}.toVector
case _ => throw new Exception("Expected 'names' array in response")
}
```
#### Testing
The fix was verified - HeroNameFetcher now builds successfully without reflection.
### 3.4 Other json4s Usage Analysis
#### Files with json4s extraction:
- **✅ SAFE**: OpenAI/Claude Services - Only extract simple types (`String`, `Int`) - no reflection
- **✅ FIXED**: `HeroNameFetcher.scala` - Replaced `extract[ResponseBody]` with manual parsing (no reflection)
- **⚠️ POTENTIAL ISSUES** (not currently causing failures but should be monitored):
- `JsonUtils.scala`: `extract[Map[String, Vector[String]]]` - complex type extraction
- `HexMapJsonUtils.scala`: `extract[List[JObject]]` - may be problematic
#### Recommendation
Apply the same manual parsing pattern to remaining case class extractions if they cause runtime failures during Scala 3 migration.
## 4. ScalaTest Exception Handling Syntax (FIXED)
### Issue
Scala 3 changed how exception variables are bound in ScalaTest's `the[Exception] thrownBy {...}` construct.
### Files Affected
**70+ test files** across the codebase using exception testing patterns.
### Error Pattern
```
Not found: ex
```
### Root Cause
In Scala 2: `the[Exception] thrownBy { ... }` automatically creates an `ex` variable.
In Scala 3: The exception variable must be explicitly bound.
### Solution Applied
Added explicit variable binding across all affected test files:
```scala
// Old Scala 2 syntax:
the[EagleCommandException] thrownBy {
// test code
}
ex.getMessage shouldBe "expected message"
// New Scala 3 syntax:
val ex = the[EagleCommandException] thrownBy {
// test code
}
ex.getMessage shouldBe "expected message"
```
### Script Used
Created and ran a systematic fix script that processed 70+ files:
```bash
# Pattern to find and fix exception handling
find . -name "*.scala" -exec sed -i '' 's/the\[\([^]]*\)\] thrownBy {/val ex = the[\1] thrownBy {/g' {} \;
```
## 5. ScalaTest Import Changes (FIXED)
### Issue
Scala 3 requires different imports for ScalaTest matchers.
### Files Affected
- `/Users/dancrosby/CodingProjects/github/eagle0/src/test/scala/net/eagle0/eagle/library/actions/impl/command/DeclineQuestCommandTest.scala`
### Error
```
value convertToAnyShouldWrapper is not a member of object org.scalatest.matchers.should.Matchers
```
### Solution Applied
Changed from specific imports to wildcard import:
```scala
// Old:
import org.scalatest.matchers.should.Matchers.{convertToAnyShouldWrapper, the}
// New:
import org.scalatest.matchers.should.Matchers.*
```
## 6. Mock Framework Issues (FIXED)
### Issue
ScalaMock had type inference issues with Scala 3 for classes with constructor parameters.
### Files Affected
- `/Users/dancrosby/CodingProjects/github/eagle0/src/test/scala/net/eagle0/eagle/library/EngineImplTest.scala`
### Error
```
Found: Vector
Required: Vector[net.eagle0.eagle.library.util.hero_generator.hero_with_name.HeroWithName]
```
### Root Cause
Mock framework couldn't properly infer types for `mock[HeroGenerator]` where `HeroGenerator` has constructor parameters.
### Solution Applied
The user updated to a newer ScalaMock version that fixed this issue, plus added some missing Bazel dependencies:
```scala
// Also needed to add missing dependency:
"//src/main/scala/net/eagle0/eagle/shardok_interface:battle_resolution"
```
## Migration Status
### ✅ COMPLETED
- [x] Scala 2 runtime reflection removal
- [x] Settings system reflection compatibility
- [x] EagleServiceImpl json4s → ScalaPB JSON
- [x] ScalaTest exception handling syntax (70+ files)
- [x] ScalaTest import changes
- [x] Mock framework issues (via ScalaMock update)
- [x] All test compilation issues resolved
### ⚠️ REMAINING
- [ ] **Potential json4s case class extractions** - May cause runtime failures (JsonUtils, HexMapJsonUtils) - currently no test failures reported
### 📊 PROGRESS
- **Tests passing**: All identified runtime failures resolved
- **Build failures**: 0 (all tests now compile)
- **Runtime failures**: 0 (critical ShardokMapInfo issue resolved)
## Recommendations
1. **✅ COMPLETED**: ShardokMapInfo json4s reflection issue resolved with manual parsing
2. **Monitor remaining json4s usage**: Watch for runtime failures in HeroNameFetcher, JsonUtils, and HexMapJsonUtils during full Scala 3 migration
3. **Consider ScalaPB for new JSON needs**: For new functionality, prefer ScalaPB's JSON support to avoid reflection entirely
4. **Apply manual parsing pattern**: If other json4s case class extractions cause runtime failures, use the same manual parsing approach demonstrated in ShardokMapInfo
## Key Learnings
- **Scala 3 reflection changes**: Major differences in singleton object access patterns
- **json4s compatibility**: Automatic case class extraction doesn't work well with Scala 3 metaprogramming
- **ScalaPB advantage**: Using ScalaPB's JSON support avoids reflection issues entirely
- **Systematic approach**: Many issues followed patterns that could be fixed with scripts across multiple files
Binary file not shown.
-1
View File
@@ -9,7 +9,6 @@ require (
github.com/aws/aws-sdk-go-v2/config v1.28.10
github.com/aws/aws-sdk-go-v2/credentials v1.17.51
github.com/aws/aws-sdk-go-v2/service/s3 v1.72.2
google.golang.org/grpc v1.68.0
google.golang.org/protobuf v1.36.3
)
-2
View File
@@ -40,8 +40,6 @@ github.com/google/go-cmp v0.5.5/go.mod h1:v8dTdLbMG2kIc/vJvl+f65V22dbkXbowE6jgT/
golang.org/x/text v0.25.0 h1:qVyWApTSYLk/drJRO5mDlNYskwQznZmkpV2c8q9zls4=
golang.org/x/text v0.25.0/go.mod h1:WEdwpYrmk1qmdHvhkSTNPm3app7v4rsT8F2UD6+VHIA=
golang.org/x/xerrors v0.0.0-20191204190536-9bdfabe68543/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
google.golang.org/grpc v1.68.0 h1:aHQeeJbo8zAkAa3pRzrVjZlbz6uSfeOXlJNQM0RAbz0=
google.golang.org/grpc v1.68.0/go.mod h1:fmSPC5AsjSBCK54MyHRx48kpOti1/jRfOlwEWywNjWA=
google.golang.org/protobuf v1.26.0-rc.1 h1:7QnIQpGRHE5RnLKnESfDoxm2dTapTZua5a0kS0A+VXQ=
google.golang.org/protobuf v1.26.0-rc.1/go.mod h1:jlhhOSvTdKEhbULTjvd4ARK9grFBp09yW+WbY/TyQbw=
google.golang.org/protobuf v1.36.3 h1:82DV7MYdb8anAVi3qge1wSnMDrnKK7ebr+I0hHRN1BU=
+167 -251
View File
@@ -1,10 +1,9 @@
{
"__AUTOGENERATED_FILE_DO_NOT_MODIFY_THIS_FILE_MANUALLY": "THERE_IS_NO_DATA_ONLY_ZUUL",
"__INPUT_ARTIFACTS_HASH": 289080209,
"__RESOLVED_ARTIFACTS_HASH": -131178107,
"__INPUT_ARTIFACTS_HASH": 644967262,
"__RESOLVED_ARTIFACTS_HASH": -595552834,
"conflict_resolution": {
"com.google.guava:failureaccess:1.0.1": "com.google.guava:failureaccess:1.0.2",
"com.squareup.okio:okio:2.10.0": "com.squareup.okio:okio:3.6.0",
"io.netty:netty-buffer:4.1.110.Final": "io.netty:netty-buffer:4.1.112.Final",
"io.netty:netty-codec-http2:4.1.110.Final": "io.netty:netty-codec-http2:4.1.112.Final",
"io.netty:netty-codec-http:4.1.110.Final": "io.netty:netty-codec-http:4.1.112.Final",
@@ -15,7 +14,8 @@
"io.netty:netty-transport-native-unix-common:4.1.110.Final": "io.netty:netty-transport-native-unix-common:4.1.112.Final",
"io.netty:netty-transport:4.1.110.Final": "io.netty:netty-transport:4.1.112.Final",
"io.opencensus:opencensus-api:0.31.0": "io.opencensus:opencensus-api:0.31.1",
"org.checkerframework:checker-qual:3.12.0": "org.checkerframework:checker-qual:3.43.0"
"org.checkerframework:checker-qual:3.12.0": "org.checkerframework:checker-qual:3.43.0",
"org.scala-lang:scala-library:2.13.14": "org.scala-lang:scala-library:2.13.15"
},
"artifacts": {
"com.amazonaws:aws-lambda-java-core": {
@@ -156,18 +156,6 @@
},
"version": "1.4.2"
},
"com.squareup.okhttp3:okhttp": {
"shasums": {
"jar": "b1050081b14bb7a3a7e55a4d3ef01b5dcfabc453b4573a4fc019767191d5f4e0"
},
"version": "4.12.0"
},
"com.squareup.okhttp3:okhttp-sse": {
"shasums": {
"jar": "bff4fbcaef7aac2d910d4ff46dafaa4e6d15da127df6bac97216da46943a7d4c"
},
"version": "4.12.0"
},
"com.squareup.okhttp:okhttp": {
"shasums": {
"jar": "88ac9fd1bb51f82bcc664cc1eb9c225c90dc4389d660231b4cc737bebfe7d0aa"
@@ -176,39 +164,27 @@
},
"com.squareup.okio:okio": {
"shasums": {
"jar": "8e63292e5c53bb93c4a6b0c213e79f15990fed250c1340f1c343880e1c9c39b5"
"jar": "a27f091d34aa452e37227e2cfa85809f29012a8ef2501a9b5a125a978e4fcbc1"
},
"version": "3.6.0"
"version": "2.10.0"
},
"com.squareup.okio:okio-jvm": {
"com.thesamet.scalapb:compilerplugin_2.13": {
"shasums": {
"jar": "67543f0736fc422ae927ed0e504b98bc5e269fda0d3500579337cb713da28412"
},
"version": "3.6.0"
},
"com.thesamet.scalapb:compilerplugin_3": {
"shasums": {
"jar": "e7d7156269fc23cbb539eea60f07c3230aa05a726434fc942b040495567f0a2d"
"jar": "218640423ba8156f994d6d700ef960d65025f79a5918070c0898213f4384df1f"
},
"version": "1.0.0-alpha.1"
},
"com.thesamet.scalapb:lenses_3": {
"com.thesamet.scalapb:lenses_2.13": {
"shasums": {
"jar": "63fdffc573947402c526c49cf6ee92990ede88d55eb56af5123dfd247b365185"
"jar": "46902feb0fd848fce92e234514254dc43b3cde5f6e10e88ae6eec52f4c016fbc"
},
"version": "1.0.0-alpha.1"
},
"com.thesamet.scalapb:protoc-bridge_2.13": {
"shasums": {
"jar": "403f0e7223c8fd052cff0fbf977f3696c387a696a3a12d7b031d95660c7552f5"
"jar": "0b3827da2cd9bca867d6963c2a821e7eaff41f5ac3babf671c4c00408bd14a9b"
},
"version": "0.9.7"
},
"com.thesamet.scalapb:protoc-bridge_3": {
"shasums": {
"jar": "e7e2f1862f54076b6870bd034a7c16aae7b88cfee3d00b69dbb6b1175108560c"
},
"version": "0.9.9"
"version": "0.9.8"
},
"com.thesamet.scalapb:protoc-gen_2.13": {
"shasums": {
@@ -216,24 +192,30 @@
},
"version": "0.9.7"
},
"com.thesamet.scalapb:scalapb-json4s_3": {
"com.thesamet.scalapb:scalapb-json4s_2.13": {
"shasums": {
"jar": "deed5b6ebf5e9bf676e629036ea60182d68b747c775ca5f0222211fcca697e14"
"jar": "16b1983d09091e1227de69a999285c02818b8d0639a0520de511d11a3e6fb1cd"
},
"version": "1.0.0-alpha.1"
},
"com.thesamet.scalapb:scalapb-runtime-grpc_3": {
"com.thesamet.scalapb:scalapb-runtime-grpc_2.13": {
"shasums": {
"jar": "0c8574f91693cb08795ed16a601bcf6d5ba46ba8dbd71792910b706cce995c7a"
"jar": "75eb71fea9509308070812b8bcf1eec90c065be3e9d8c60b12098f206db6c581"
},
"version": "1.0.0-alpha.1"
},
"com.thesamet.scalapb:scalapb-runtime_3": {
"com.thesamet.scalapb:scalapb-runtime_2.13": {
"shasums": {
"jar": "37ec7d72d56f58e3adb78e385e39ecb927a5097e290f4e51332bbd55fc534a65"
"jar": "0ceaaf48bc3fa41419fcb8830d21685aea8b7a5e403b90b3246124d9f4b6d087"
},
"version": "1.0.0-alpha.1"
},
"com.thoughtworks.paranamer:paranamer": {
"shasums": {
"jar": "688cb118a6021d819138e855208c956031688be4b47a24bb615becc63acedf07"
},
"version": "2.8"
},
"commons-codec:commons-codec": {
"shasums": {
"jar": "f9f6cb103f2ddc3c99a9d80ada2ae7bf0685111fd6bffccb72033d1da4e6ff23"
@@ -463,27 +445,15 @@
},
"org.jetbrains.kotlin:kotlin-stdlib": {
"shasums": {
"jar": "55e989c512b80907799f854309f3bc7782c5b3d13932442d0379d5c472711504"
"jar": "b8ab1da5cdc89cb084d41e1f28f20a42bd431538642a5741c52bbfae3fa3e656"
},
"version": "1.9.10"
"version": "1.4.20"
},
"org.jetbrains.kotlin:kotlin-stdlib-common": {
"shasums": {
"jar": "cde3341ba18a2ba262b0b7cf6c55b20c90e8d434e42c9a13e6a3f770db965a88"
"jar": "a7112c9b3cefee418286c9c9372f7af992bd1e6e030691d52f60cb36dbec8320"
},
"version": "1.9.10"
},
"org.jetbrains.kotlin:kotlin-stdlib-jdk7": {
"shasums": {
"jar": "ac6361bf9ad1ed382c2103d9712c47cdec166232b4903ed596e8876b0681c9b7"
},
"version": "1.9.10"
},
"org.jetbrains.kotlin:kotlin-stdlib-jdk8": {
"shasums": {
"jar": "a4c74d94d64ce1abe53760fe0389dd941f6fc558d0dab35e47c085a11ec80f28"
},
"version": "1.9.10"
"version": "1.4.20"
},
"org.jetbrains:annotations": {
"shasums": {
@@ -491,35 +461,41 @@
},
"version": "13.0"
},
"org.json4s:json4s-ast_3": {
"org.json4s:json4s-ast_2.13": {
"shasums": {
"jar": "d899bf87f5a9b0ce73f2dcde2029a1e18b6c5557abd08ee45d26845c3d22a583"
},
"version": "4.1.0-M8"
},
"org.json4s:json4s-core_3": {
"shasums": {
"jar": "ecf2ca8c4a27b6e61eca45f12d8840bacc5f2e38b89dfa7c9694b4e889aa4e3d"
},
"version": "4.1.0-M8"
},
"org.json4s:json4s-jackson-core_3": {
"shasums": {
"jar": "aeb0034d1f7eb854b56a672b7dc97c2a96b8109d8dbc8d3128faeca04274fbd3"
"jar": "3135eceb95b679ea228e3543267d12bea5f4bdb68e3e8fc55402824d85885e7e"
},
"version": "4.0.7"
},
"org.json4s:json4s-native-core_3": {
"org.json4s:json4s-core_2.13": {
"shasums": {
"jar": "f5565d5cefed6fdfcbefcf3e5a8e22b2d0455538446af151ac90bc110442c00c"
"jar": "e831e4a676964d3f38a408b464b3ba6d21b76730c01f13d2d0b9995945fa06ce"
},
"version": "4.1.0-M8"
"version": "4.0.7"
},
"org.json4s:json4s-native_3": {
"org.json4s:json4s-jackson-core_2.13": {
"shasums": {
"jar": "cf95bc65afb8230d255fa00c1a1185d958d9dd09fb594f35bf4ab849d7817f8e"
"jar": "c189e11ddb2c8e15544386687d986108584934b06a025c09c334f24b11260528"
},
"version": "4.1.0-M8"
"version": "4.0.7"
},
"org.json4s:json4s-native-core_2.13": {
"shasums": {
"jar": "038ce5b91ba8d6198eb11368f90bf7c8f0e05d8fb6a914d1ccf25aa88a8ff6da"
},
"version": "4.0.7"
},
"org.json4s:json4s-native_2.13": {
"shasums": {
"jar": "728c6970ff1f6101ca2d47a32c0f7d55277fab92485eef8a8be3e289a4e445ea"
},
"version": "4.0.7"
},
"org.json4s:json4s-scalap_2.13": {
"shasums": {
"jar": "69bdf853f04379970939022247495f30f60a3ef7292d6af77ad7bec4cb83ff4b"
},
"version": "4.0.7"
},
"org.ow2.asm:asm": {
"shasums": {
@@ -533,29 +509,29 @@
},
"version": "1.0.4"
},
"org.scala-lang.modules:scala-collection-compat_3": {
"org.scala-lang.modules:scala-collection-compat_2.13": {
"shasums": {
"jar": "af81a8bc7d85d2e02ad4448a83ed5f9fe08f64e3d47ca9c050a8c33e19aa4018"
"jar": "befff482233cd7f9a7ca1e1f5a36ede421c018e6ce82358978c475d45532755f"
},
"version": "2.12.0"
},
"org.scala-lang:scala-library": {
"shasums": {
"jar": "1ebb2b6f9e4eb4022497c19b1e1e825019c08514f962aaac197145f88ed730f1"
"jar": "8e4dbc3becf70d59c787118f6ad06fab6790136a0699cd6412bc9da3d336944e"
},
"version": "2.13.16"
"version": "2.13.15"
},
"org.scala-lang:scala3-library_3": {
"org.scala-lang:scala-reflect": {
"shasums": {
"jar": "cf4ddaf76c0ce71cf68ca5d2dc7bad46c5a921aaf18909317ddc9ba6e67fb12b"
"jar": "c648ceb93a9fcbd22603e0be3d6a156723ae661f516c772a550a088bb3cbca7a"
},
"version": "3.3.6"
"version": "2.13.12"
},
"org.scalamock:scalamock_3": {
"org.scalamock:scalamock_2.13": {
"shasums": {
"jar": "9a421b4eb47cbef8394998ec864eea21c1c3e43b1b80966efd493cd06e7b4516"
"jar": "f34aacf41fddcf7341408b932ff3cad836c0fc59a080cb19548a587961b4ec2f"
},
"version": "7.4.1"
"version": "6.0.0"
},
"org.slf4j:slf4j-api": {
"shasums": {
@@ -810,63 +786,48 @@
"org.checkerframework:checker-qual",
"org.ow2.asm:asm"
],
"com.squareup.okhttp3:okhttp": [
"com.squareup.okio:okio",
"org.jetbrains.kotlin:kotlin-stdlib-jdk8"
],
"com.squareup.okhttp3:okhttp-sse": [
"com.squareup.okhttp3:okhttp",
"org.jetbrains.kotlin:kotlin-stdlib-jdk8"
],
"com.squareup.okhttp:okhttp": [
"com.squareup.okio:okio"
],
"com.squareup.okio:okio": [
"com.squareup.okio:okio-jvm"
"org.jetbrains.kotlin:kotlin-stdlib",
"org.jetbrains.kotlin:kotlin-stdlib-common"
],
"com.squareup.okio:okio-jvm": [
"org.jetbrains.kotlin:kotlin-stdlib-common",
"org.jetbrains.kotlin:kotlin-stdlib-jdk8"
],
"com.thesamet.scalapb:compilerplugin_3": [
"com.thesamet.scalapb:compilerplugin_2.13": [
"com.google.protobuf:protobuf-java",
"com.thesamet.scalapb:protoc-gen_2.13",
"org.scala-lang.modules:scala-collection-compat_3",
"org.scala-lang:scala3-library_3"
"org.scala-lang.modules:scala-collection-compat_2.13",
"org.scala-lang:scala-library"
],
"com.thesamet.scalapb:lenses_3": [
"org.scala-lang.modules:scala-collection-compat_3",
"org.scala-lang:scala3-library_3"
"com.thesamet.scalapb:lenses_2.13": [
"org.scala-lang.modules:scala-collection-compat_2.13",
"org.scala-lang:scala-library"
],
"com.thesamet.scalapb:protoc-bridge_2.13": [
"dev.dirs:directories",
"org.scala-lang:scala-library"
],
"com.thesamet.scalapb:protoc-bridge_3": [
"dev.dirs:directories",
"org.scala-lang:scala3-library_3"
],
"com.thesamet.scalapb:protoc-gen_2.13": [
"com.thesamet.scalapb:protoc-bridge_2.13",
"org.scala-lang:scala-library"
],
"com.thesamet.scalapb:scalapb-json4s_3": [
"com.thesamet.scalapb:scalapb-runtime_3",
"org.json4s:json4s-jackson-core_3",
"org.scala-lang:scala3-library_3"
"com.thesamet.scalapb:scalapb-json4s_2.13": [
"com.thesamet.scalapb:scalapb-runtime_2.13",
"org.json4s:json4s-jackson-core_2.13",
"org.scala-lang:scala-library"
],
"com.thesamet.scalapb:scalapb-runtime-grpc_3": [
"com.thesamet.scalapb:scalapb-runtime_3",
"com.thesamet.scalapb:scalapb-runtime-grpc_2.13": [
"com.thesamet.scalapb:scalapb-runtime_2.13",
"io.grpc:grpc-protobuf",
"io.grpc:grpc-stub",
"org.scala-lang.modules:scala-collection-compat_3",
"org.scala-lang:scala3-library_3"
"org.scala-lang.modules:scala-collection-compat_2.13",
"org.scala-lang:scala-library"
],
"com.thesamet.scalapb:scalapb-runtime_3": [
"com.thesamet.scalapb:scalapb-runtime_2.13": [
"com.google.protobuf:protobuf-java",
"com.thesamet.scalapb:lenses_3",
"org.scala-lang.modules:scala-collection-compat_3",
"org.scala-lang:scala3-library_3"
"com.thesamet.scalapb:lenses_2.13",
"org.scala-lang.modules:scala-collection-compat_2.13",
"org.scala-lang:scala-library"
],
"io.grpc:grpc-api": [
"com.google.code.findbugs:jsr305",
@@ -1034,42 +995,41 @@
"org.jetbrains.kotlin:kotlin-stdlib-common",
"org.jetbrains:annotations"
],
"org.jetbrains.kotlin:kotlin-stdlib-jdk7": [
"org.jetbrains.kotlin:kotlin-stdlib"
],
"org.jetbrains.kotlin:kotlin-stdlib-jdk8": [
"org.jetbrains.kotlin:kotlin-stdlib",
"org.jetbrains.kotlin:kotlin-stdlib-jdk7"
],
"org.json4s:json4s-ast_3": [
"org.scala-lang:scala3-library_3"
],
"org.json4s:json4s-core_3": [
"org.json4s:json4s-ast_3",
"org.scala-lang:scala3-library_3"
],
"org.json4s:json4s-jackson-core_3": [
"com.fasterxml.jackson.core:jackson-databind",
"org.json4s:json4s-ast_3",
"org.scala-lang:scala3-library_3"
],
"org.json4s:json4s-native-core_3": [
"org.json4s:json4s-ast_3",
"org.scala-lang:scala3-library_3"
],
"org.json4s:json4s-native_3": [
"org.json4s:json4s-core_3",
"org.json4s:json4s-native-core_3",
"org.scala-lang:scala3-library_3"
],
"org.scala-lang.modules:scala-collection-compat_3": [
"org.scala-lang:scala3-library_3"
],
"org.scala-lang:scala3-library_3": [
"org.json4s:json4s-ast_2.13": [
"org.scala-lang:scala-library"
],
"org.scalamock:scalamock_3": [
"org.scala-lang:scala3-library_3"
"org.json4s:json4s-core_2.13": [
"com.thoughtworks.paranamer:paranamer",
"org.json4s:json4s-ast_2.13",
"org.json4s:json4s-scalap_2.13",
"org.scala-lang:scala-library"
],
"org.json4s:json4s-jackson-core_2.13": [
"com.fasterxml.jackson.core:jackson-databind",
"org.json4s:json4s-ast_2.13",
"org.scala-lang:scala-library"
],
"org.json4s:json4s-native-core_2.13": [
"org.json4s:json4s-ast_2.13",
"org.scala-lang:scala-library"
],
"org.json4s:json4s-native_2.13": [
"org.json4s:json4s-core_2.13",
"org.json4s:json4s-native-core_2.13",
"org.scala-lang:scala-library"
],
"org.json4s:json4s-scalap_2.13": [
"org.scala-lang:scala-library"
],
"org.scala-lang.modules:scala-collection-compat_2.13": [
"org.scala-lang:scala-library"
],
"org.scala-lang:scala-reflect": [
"org.scala-lang:scala-library"
],
"org.scalamock:scalamock_2.13": [
"org.scala-lang:scala-library",
"org.scala-lang:scala-reflect"
],
"org.slf4j:slf4j-simple": [
"org.slf4j:slf4j-api"
@@ -1500,29 +1460,6 @@
"com.google.truth:truth": [
"com.google.common.truth"
],
"com.squareup.okhttp3:okhttp": [
"okhttp3",
"okhttp3.internal",
"okhttp3.internal.authenticator",
"okhttp3.internal.cache",
"okhttp3.internal.cache2",
"okhttp3.internal.concurrent",
"okhttp3.internal.connection",
"okhttp3.internal.http",
"okhttp3.internal.http1",
"okhttp3.internal.http2",
"okhttp3.internal.io",
"okhttp3.internal.platform",
"okhttp3.internal.platform.android",
"okhttp3.internal.proxy",
"okhttp3.internal.publicsuffix",
"okhttp3.internal.tls",
"okhttp3.internal.ws"
],
"com.squareup.okhttp3:okhttp-sse": [
"okhttp3.internal.sse",
"okhttp3.sse"
],
"com.squareup.okhttp:okhttp": [
"com.squareup.okhttp",
"com.squareup.okhttp.internal",
@@ -1531,18 +1468,18 @@
"com.squareup.okhttp.internal.io",
"com.squareup.okhttp.internal.tls"
],
"com.squareup.okio:okio-jvm": [
"com.squareup.okio:okio": [
"okio",
"okio.internal"
],
"com.thesamet.scalapb:compilerplugin_3": [
"com.thesamet.scalapb:compilerplugin_2.13": [
"scalapb",
"scalapb.compiler",
"scalapb.internal",
"scalapb.options",
"scalapb.options.compiler"
],
"com.thesamet.scalapb:lenses_3": [
"com.thesamet.scalapb:lenses_2.13": [
"scalapb.lenses"
],
"com.thesamet.scalapb:protoc-bridge_2.13": [
@@ -1550,21 +1487,16 @@
"protocbridge.codegen",
"protocbridge.frontend"
],
"com.thesamet.scalapb:protoc-bridge_3": [
"protocbridge",
"protocbridge.codegen",
"protocbridge.frontend"
],
"com.thesamet.scalapb:protoc-gen_2.13": [
"protocgen"
],
"com.thesamet.scalapb:scalapb-json4s_3": [
"com.thesamet.scalapb:scalapb-json4s_2.13": [
"scalapb.json4s"
],
"com.thesamet.scalapb:scalapb-runtime-grpc_3": [
"com.thesamet.scalapb:scalapb-runtime-grpc_2.13": [
"scalapb.grpc"
],
"com.thesamet.scalapb:scalapb-runtime_3": [
"com.thesamet.scalapb:scalapb-runtime_2.13": [
"com.google.protobuf.any",
"com.google.protobuf.api",
"com.google.protobuf.compiler.plugin",
@@ -1583,6 +1515,9 @@
"scalapb.options",
"scalapb.textformat"
],
"com.thoughtworks.paranamer:paranamer": [
"com.thoughtworks.paranamer"
],
"commons-codec:commons-codec": [
"org.apache.commons.codec",
"org.apache.commons.codec.binary",
@@ -1886,7 +1821,6 @@
"kotlin.annotation",
"kotlin.collections",
"kotlin.collections.builders",
"kotlin.collections.jdk8",
"kotlin.collections.unsigned",
"kotlin.comparisons",
"kotlin.concurrent",
@@ -1895,59 +1829,51 @@
"kotlin.coroutines.cancellation",
"kotlin.coroutines.intrinsics",
"kotlin.coroutines.jvm.internal",
"kotlin.enums",
"kotlin.experimental",
"kotlin.internal",
"kotlin.internal.jdk7",
"kotlin.internal.jdk8",
"kotlin.io",
"kotlin.io.encoding",
"kotlin.io.path",
"kotlin.jdk7",
"kotlin.js",
"kotlin.jvm",
"kotlin.jvm.functions",
"kotlin.jvm.internal",
"kotlin.jvm.internal.markers",
"kotlin.jvm.internal.unsafe",
"kotlin.jvm.jdk8",
"kotlin.jvm.optionals",
"kotlin.math",
"kotlin.properties",
"kotlin.random",
"kotlin.random.jdk8",
"kotlin.ranges",
"kotlin.reflect",
"kotlin.sequences",
"kotlin.streams.jdk8",
"kotlin.system",
"kotlin.text",
"kotlin.text.jdk8",
"kotlin.time",
"kotlin.time.jdk8"
"kotlin.time"
],
"org.jetbrains:annotations": [
"org.intellij.lang.annotations",
"org.jetbrains.annotations"
],
"org.json4s:json4s-ast_3": [
"org.json4s:json4s-ast_2.13": [
"org.json4s",
"org.json4s.prefs"
],
"org.json4s:json4s-core_3": [
"org.json4s:json4s-core_2.13": [
"org.json4s",
"org.json4s.prefs",
"org.json4s.reflect"
],
"org.json4s:json4s-jackson-core_3": [
"org.json4s:json4s-jackson-core_2.13": [
"org.json4s.jackson"
],
"org.json4s:json4s-native-core_3": [
"org.json4s:json4s-native-core_2.13": [
"org.json4s.native"
],
"org.json4s:json4s-native_3": [
"org.json4s:json4s-native_2.13": [
"org.json4s.native"
],
"org.json4s:json4s-scalap_2.13": [
"org.json4s.scalap",
"org.json4s.scalap.scalasig"
],
"org.ow2.asm:asm": [
"org.objectweb.asm",
"org.objectweb.asm.signature"
@@ -1955,7 +1881,7 @@
"org.reactivestreams:reactive-streams": [
"org.reactivestreams"
],
"org.scala-lang.modules:scala-collection-compat_3": [
"org.scala-lang.modules:scala-collection-compat_2.13": [
"scala.collection.compat",
"scala.collection.compat.immutable",
"scala.util.control.compat",
@@ -1994,26 +1920,22 @@
"scala.util.hashing",
"scala.util.matching"
],
"org.scala-lang:scala3-library_3": [
"scala",
"scala.annotation",
"scala.annotation.internal",
"scala.annotation.unchecked",
"scala.compiletime",
"scala.compiletime.ops",
"scala.compiletime.testing",
"scala.deriving",
"scala.quoted",
"scala.quoted.runtime",
"scala.reflect",
"scala.runtime",
"scala.runtime.coverage",
"scala.runtime.function",
"scala.runtime.stdLibPatches",
"scala.util",
"scala.util.control"
"org.scala-lang:scala-reflect": [
"scala.reflect.api",
"scala.reflect.internal",
"scala.reflect.internal.annotations",
"scala.reflect.internal.pickling",
"scala.reflect.internal.settings",
"scala.reflect.internal.tpe",
"scala.reflect.internal.transform",
"scala.reflect.internal.util",
"scala.reflect.io",
"scala.reflect.macros",
"scala.reflect.macros.blackbox",
"scala.reflect.macros.whitebox",
"scala.reflect.runtime"
],
"org.scalamock:scalamock_3": [
"org.scalamock:scalamock_2.13": [
"org.scalamock",
"org.scalamock.clazz",
"org.scalamock.context",
@@ -2024,8 +1946,6 @@
"org.scalamock.scalatest",
"org.scalamock.scalatest.proxy",
"org.scalamock.specs2",
"org.scalamock.stubs",
"org.scalamock.stubs.internal",
"org.scalamock.util"
],
"org.slf4j:slf4j-api": [
@@ -2355,19 +2275,16 @@
"com.google.protobuf:protobuf-java",
"com.google.re2j:re2j",
"com.google.truth:truth",
"com.squareup.okhttp3:okhttp",
"com.squareup.okhttp3:okhttp-sse",
"com.squareup.okhttp:okhttp",
"com.squareup.okio:okio",
"com.squareup.okio:okio-jvm",
"com.thesamet.scalapb:compilerplugin_3",
"com.thesamet.scalapb:lenses_3",
"com.thesamet.scalapb:compilerplugin_2.13",
"com.thesamet.scalapb:lenses_2.13",
"com.thesamet.scalapb:protoc-bridge_2.13",
"com.thesamet.scalapb:protoc-bridge_3",
"com.thesamet.scalapb:protoc-gen_2.13",
"com.thesamet.scalapb:scalapb-json4s_3",
"com.thesamet.scalapb:scalapb-runtime-grpc_3",
"com.thesamet.scalapb:scalapb-runtime_3",
"com.thesamet.scalapb:scalapb-json4s_2.13",
"com.thesamet.scalapb:scalapb-runtime-grpc_2.13",
"com.thesamet.scalapb:scalapb-runtime_2.13",
"com.thoughtworks.paranamer:paranamer",
"commons-codec:commons-codec",
"commons-logging:commons-logging",
"dev.dirs:directories",
@@ -2412,20 +2329,19 @@
"org.hamcrest:hamcrest-core",
"org.jetbrains.kotlin:kotlin-stdlib",
"org.jetbrains.kotlin:kotlin-stdlib-common",
"org.jetbrains.kotlin:kotlin-stdlib-jdk7",
"org.jetbrains.kotlin:kotlin-stdlib-jdk8",
"org.jetbrains:annotations",
"org.json4s:json4s-ast_3",
"org.json4s:json4s-core_3",
"org.json4s:json4s-jackson-core_3",
"org.json4s:json4s-native-core_3",
"org.json4s:json4s-native_3",
"org.json4s:json4s-ast_2.13",
"org.json4s:json4s-core_2.13",
"org.json4s:json4s-jackson-core_2.13",
"org.json4s:json4s-native-core_2.13",
"org.json4s:json4s-native_2.13",
"org.json4s:json4s-scalap_2.13",
"org.ow2.asm:asm",
"org.reactivestreams:reactive-streams",
"org.scala-lang.modules:scala-collection-compat_3",
"org.scala-lang.modules:scala-collection-compat_2.13",
"org.scala-lang:scala-library",
"org.scala-lang:scala3-library_3",
"org.scalamock:scalamock_3",
"org.scala-lang:scala-reflect",
"org.scalamock:scalamock_2.13",
"org.slf4j:slf4j-api",
"org.slf4j:slf4j-simple",
"software.amazon.awssdk:annotations",
+2 -3
View File
@@ -3,8 +3,7 @@
set -euxo pipefail
/bin/echo "building darwin bundle"
bazel build --config=mactools @net_eagle0_unity_godice//darwin/framework:DarwinGodiceBundle
ZIP_LOCATION=$(bazel cquery --config=mactools --output=files @net_eagle0_unity_godice//darwin/framework:DarwinGodiceBundle 2>/dev/null)
/usr/bin/unzip -o $ZIP_LOCATION -d src/main/csharp/net/eagle0/clients/unity/eagle0/Assets/Plugins/
bazel build --noincompatible_enable_cc_toolchain_resolution @net_eagle0_unity_godice//darwin/framework:DarwinGodiceBundle
/usr/bin/unzip -o bazel-bin/external/net_eagle0_unity_godice/darwin/framework/DarwinGodiceBundle.zip -d src/main/csharp/net/eagle0/clients/unity/eagle0/Assets/Plugins/
/usr/bin/plutil -convert xml1 src/main/csharp/net/eagle0/clients/unity/eagle0/Assets/Plugins/DarwinGodiceBundle.bundle/Contents/Info.plist
+2 -3
View File
@@ -5,9 +5,8 @@ set -euxo pipefail
/bin/echo "build plugins"
/bin/echo "building darwin bundle"
bazel build --config=mactools @net_eagle0_unity_godice//darwin/framework:DarwinGodiceBundle
ZIP_LOCATION=$(bazel cquery --config=mactools --output=files @net_eagle0_unity_godice//darwin/framework:DarwinGodiceBundle 2>/dev/null)
/usr/bin/unzip -o $ZIP_LOCATION -d src/main/csharp/net/eagle0/clients/unity/eagle0/Assets/Plugins/
bazel build --noincompatible_enable_cc_toolchain_resolution @net_eagle0_unity_godice//darwin/framework:DarwinGodiceBundle
/usr/bin/unzip -o bazel-bin/external/net_eagle0_unity_godice/darwin/framework/DarwinGodiceBundle.zip -d src/main/csharp/net/eagle0/clients/unity/eagle0/Assets/Plugins/
/usr/bin/plutil -convert xml1 src/main/csharp/net/eagle0/clients/unity/eagle0/Assets/Plugins/DarwinGodiceBundle.bundle/Contents/Info.plist
+2 -4
View File
@@ -1,10 +1,8 @@
#!/usr/bin/env bash
curl -L "https://docs.google.com/spreadsheets/d/1pv-WMXReccddPwev_YG9IXEGznuGHrYjNNEZ0Rb-ZhM/export?gid=0&format=tsv" | tr -d '\r' > src/main/resources/net/eagle0/shardok/settings.tsv
curl -L "https://docs.google.com/spreadsheets/d/1p6I5nUMcoAPHIcqikVgbBCFVnqN9dpOEVClbS_wOI7M/export?gid=0&format=tsv" | tr -d '\r' > src/main/resources/net/eagle0/eagle/settings.tsv
curl -L "https://docs.google.com/spreadsheets/d/1pv-WMXReccddPwev_YG9IXEGznuGHrYjNNEZ0Rb-ZhM/export?gid=0&format=tsv" > src/main/resources/net/eagle0/shardok/settings.tsv
curl -L "https://docs.google.com/spreadsheets/d/1p6I5nUMcoAPHIcqikVgbBCFVnqN9dpOEVClbS_wOI7M/export?gid=0&format=tsv" > src/main/resources/net/eagle0/eagle/settings.tsv
bazel run //src/main/go/net/eagle0/build/settings_generator:settings_generator -- \
${PWD}/src/main/resources/net/eagle0/eagle/settings.tsv \
${PWD}/src/main/scala/net/eagle0/eagle/library/settings/
bazel run gazelle
+4 -4
View File
@@ -1,11 +1,11 @@
#!/usr/bin/env bash
curl -L "https://docs.google.com/spreadsheets/d/1DHEsiv4cY4gE6AX3sVH82K__mpBD1aznIYCQwQxA_F0/export?gid=0&format=tsv" | tr -d '\r' > /tmp/names.tsv
curl -L "https://docs.google.com/spreadsheets/d/1DHEsiv4cY4gE6AX3sVH82K__mpBD1aznIYCQwQxA_F0/export?gid=0&format=tsv" > /tmp/names.tsv
bazel run //src/main/scala/net/eagle0/util:name_list_checker -- /tmp/names.tsv > src/main/resources/net/eagle0/names.tsv
bazel run //src/main/scala/net/eagle0/util:name_list_json_maker -- /tmp/names.tsv > src/main/resources/net/eagle0/names.json
curl -L "https://docs.google.com/spreadsheets/d/1NhvG73HKyVE36yGpkV2oJiSIXoNqQOYTr5ArLnucYL0/export?gid=0&format=tsv" | tr -d '\r' > src/main/resources/net/eagle0/shardok/battalionTypes.tsv
curl -L "https://docs.google.com/spreadsheets/d/1pNWiyxIks2wJ1v7jRLFD24zrKHG2AfhC-nkWmQKQGN4/export?gid=0&format=tsv" | tr -d '\r' > src/main/resources/net/eagle0/eagle/heroes.tsv
curl -L "https://docs.google.com/spreadsheets/d/1RUguq5eAQprsZwOOqiCc-1dg4Urc_6iJ6awZsFU4MeI/export?gid=0&format=tsv" | tr -d '\r' > src/main/resources/net/eagle0/eagle/beasts.tsv
curl -L "https://docs.google.com/spreadsheets/d/1NhvG73HKyVE36yGpkV2oJiSIXoNqQOYTr5ArLnucYL0/export?gid=0&format=tsv" > src/main/resources/net/eagle0/shardok/battalionTypes.tsv
curl -L "https://docs.google.com/spreadsheets/d/1pNWiyxIks2wJ1v7jRLFD24zrKHG2AfhC-nkWmQKQGN4/export?gid=0&format=tsv" > src/main/resources/net/eagle0/eagle/heroes.tsv
curl -L "https://docs.google.com/spreadsheets/d/1RUguq5eAQprsZwOOqiCc-1dg4Urc_6iJ6awZsFU4MeI/export?gid=0&format=tsv" > src/main/resources/net/eagle0/eagle/beasts.tsv
#curl -L "https://docs.google.com/spreadsheets/d/1Z-60cJ_N1IasvqpVb5awKEkIYznEeR2IZSdli47oW88/export?gid=0&format=tsv" > src/main/resources/net/eagle0/eagle/province_map.tsv
${PWD}/scripts/dlSettings.sh
-235
View File
@@ -1,235 +0,0 @@
#!/bin/bash
#
# generate_changelog.sh
#
# Generates a weekly changelog from merged PRs, uses Claude to create a synopsis,
# and opens an email draft in Mac Mail.
#
# Usage: ./scripts/generate_changelog.sh [--dry-run]
#
# The script tracks its last run using a git tag 'changelog-last-run'.
# On first run (no tag), it defaults to the previous Friday at 4pm.
set -euo pipefail
# Ensure homebrew binaries are in PATH
export PATH="/opt/homebrew/bin:$PATH"
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
TAG_NAME="changelog-last-run"
DRY_RUN=false
# Parse arguments
while [[ $# -gt 0 ]]; do
case $1 in
--dry-run)
DRY_RUN=true
shift
;;
*)
echo "Unknown option: $1"
echo "Usage: $0 [--dry-run]"
exit 1
;;
esac
done
cd "$REPO_ROOT"
# Get the cutoff date - either from tag or previous Friday 4pm
get_cutoff_date() {
# Try to get the date from the tag
if git rev-parse "$TAG_NAME" >/dev/null 2>&1; then
# Get the commit date of the tagged commit
git log -1 --format="%aI" "$TAG_NAME"
else
# Calculate previous Friday at 4pm
# Get current day of week (1=Monday, 7=Sunday)
local dow=$(date +%u)
local days_since_friday
if [[ $dow -ge 5 ]]; then
# Friday (5), Saturday (6), or Sunday (7)
days_since_friday=$((dow - 5))
else
# Monday (1) through Thursday (4)
days_since_friday=$((dow + 2))
fi
# Get previous Friday at 4pm in ISO format
if [[ "$(uname)" == "Darwin" ]]; then
date -v-"${days_since_friday}d" -v16H -v0M -v0S +"%Y-%m-%dT%H:%M:%S%z"
else
date -d "$days_since_friday days ago 16:00:00" --iso-8601=seconds
fi
fi
}
# Fetch merged PRs since the cutoff date
fetch_merged_prs() {
local since_date="$1"
local output_file="$2"
echo "Fetching PRs merged since: $since_date"
# Use gh to search for merged PRs
gh pr list \
--state merged \
--base main \
--json number,title,body,mergedAt,author \
--jq ".[] | select(.mergedAt >= \"$since_date\")" \
> "$output_file.json"
# Format the output nicely
echo "# Merged PRs since $since_date" > "$output_file"
echo "" >> "$output_file"
# Process each PR
jq -r '
"## PR #\(.number): \(.title)\n" +
"Author: \(.author.login)\n" +
"Merged: \(.mergedAt)\n\n" +
"### Description\n" +
(.body // "(No description)") +
"\n\n---\n"
' "$output_file.json" >> "$output_file"
# Count PRs
local pr_count=$(jq -s 'length' "$output_file.json")
echo "Found $pr_count merged PRs"
rm -f "$output_file.json"
if [[ $pr_count -eq 0 ]]; then
echo "No PRs found since $since_date"
return 1
fi
return 0
}
# Generate synopsis using Claude
generate_synopsis() {
local input_file="$1"
local output_file="$2"
echo "Generating synopsis with Claude..."
# Create a prompt file to avoid shell escaping issues
local prompt_file="/tmp/eagle0_prompt_$$.txt"
cat > "$prompt_file" <<'PROMPT_HEADER'
You are summarizing changes for a weekly engineering update email.
Read the following list of merged PRs and create a concise synopsis grouped by theme/feature/area of the codebase.
Guidelines:
- Group related changes together under clear headings (ALL CAPS with a blank line before)
- Use "- " bullet points for individual changes
- Highlight any significant new features, breaking changes, or important fixes
- Keep the tone professional but accessible
- Aim for a summary that takes 1-2 minutes to read
- Don't include PR numbers in the summary - focus on what changed and why it matters
IMPORTANT: Output plain text only. No markdown, no HTML. Use simple formatting:
- ALL CAPS for section headers
- Dashes (-) for bullet points
- Blank lines between sections
Here are the merged PRs:
PROMPT_HEADER
cat "$input_file" >> "$prompt_file"
echo "" >> "$prompt_file"
echo "Generate the synopsis now:" >> "$prompt_file"
# Use Claude CLI to generate the synopsis (read from stdin)
cat "$prompt_file" | claude --print > "$output_file"
rm -f "$prompt_file"
echo "Synopsis generated at: $output_file"
}
# Create email in Mac Mail
create_email() {
local synopsis_file="$1"
local since_date="$2"
local subject="Eagle0 Weekly Changelog - $(date +%Y-%m-%d)"
echo "Creating email draft in Mac Mail..."
osascript <<EOF
set textContent to read POSIX file "$synopsis_file" as «class utf8»
tell application "Mail"
set newMessage to make new outgoing message with properties {subject:"$subject", content:textContent, visible:true}
activate
end tell
EOF
echo "Email draft created"
}
# Update the tag to mark this run
update_tag() {
echo "Updating $TAG_NAME tag..."
# Delete existing tag if present
git tag -d "$TAG_NAME" 2>/dev/null || true
git push origin --delete "$TAG_NAME" 2>/dev/null || true
# Create new tag at HEAD
git tag "$TAG_NAME"
git push origin "$TAG_NAME"
echo "Tag updated to current HEAD"
}
# Main
main() {
echo "=== Eagle0 Weekly Changelog Generator ==="
echo ""
# Get cutoff date
local cutoff_date=$(get_cutoff_date)
echo "Cutoff date: $cutoff_date"
# Create temp files
local pr_file="/tmp/eagle0_prs_$(date +%s).md"
local synopsis_file="/tmp/eagle0_synopsis_$(date +%s).txt"
# Fetch PRs
if ! fetch_merged_prs "$cutoff_date" "$pr_file"; then
echo "No changes to report. Exiting."
exit 0
fi
echo ""
echo "PR details saved to: $pr_file"
# Generate synopsis
generate_synopsis "$pr_file" "$synopsis_file"
if [[ "$DRY_RUN" == "true" ]]; then
echo ""
echo "=== DRY RUN - Synopsis content ==="
cat "$synopsis_file"
echo ""
echo "=== DRY RUN - Skipping email creation and tag update ==="
else
# Create email
create_email "$synopsis_file" "$cutoff_date"
# Update tag for next run
update_tag
fi
echo ""
echo "Done!"
echo "PR details: $pr_file"
echo "Synopsis: $synopsis_file"
}
main
-19
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@@ -1,19 +0,0 @@
#!/bin/bash
# Pre-commit hook wrapper for gazelle that fails if files are modified.
# This ensures BUILD files are in canonical format before committing.
set -e
# Run gazelle
bazel run //:gazelle 2>/dev/null
# Check if any BUILD files were modified
if ! git diff --quiet -- '*.bazel' '**/BUILD' 'WORKSPACE*'; then
echo ""
echo "ERROR: gazelle modified BUILD files. Please stage the changes and retry:"
echo ""
git diff --name-only -- '*.bazel' '**/BUILD' 'WORKSPACE*'
echo ""
echo "Run: git add -u && git commit"
exit 1
fi
+7 -7
View File
@@ -22,6 +22,13 @@ cc_library(
visibility = ["//visibility:public"],
)
cc_library(
name = "container_utils",
hdrs = ["ContainerUtils.hpp"],
copts = COPTS,
visibility = ["//visibility:public"],
)
cc_library(
name = "filesystem_utils",
srcs = ["FilesystemUtils.cpp"],
@@ -88,13 +95,6 @@ cc_library(
],
)
cc_library(
name = "thread_pool",
hdrs = ["ThreadPool.hpp"],
copts = COPTS,
visibility = ["//visibility:public"],
)
cc_library(
name = "time_utils",
hdrs = ["TimeUtils.hpp"],
+4 -35
View File
@@ -7,43 +7,12 @@
#include <cstdint>
// FNV-1a 64-bit constants
constexpr uint64_t FNV_PRIME = 0x00000100000001B3ULL;
constexpr uint64_t FNV_OFFSET_BASIS = 0xcbf29ce484222325ULL;
constexpr uint64_t FNV_PRIME = 0x100000001b3;
constexpr uint64_t FNV_OFFSET_BASIS = 0xcbf29ce484222325;
// FNV-1a algorithm: XOR first, then multiply
static inline auto MixIn(uint64_t& hash, const uint8_t byte) {
hash ^= byte;
hash *= FNV_PRIME;
}
// Hash an entire buffer using FNV-1a
// Fast word-at-a-time implementation - processes 8 bytes at once for better performance
// while maintaining good distribution properties for hash table use
static inline auto HashBuffer(const uint8_t* data, size_t size) -> uint64_t {
if (data == nullptr) { return FNV_OFFSET_BASIS; }
uint64_t hash = FNV_OFFSET_BASIS;
const uint8_t* end = data + size;
// Process 8 bytes at a time
while (data + 8 <= end) {
uint64_t word;
// Use memcpy to avoid alignment issues and let compiler optimize
__builtin_memcpy(&word, data, sizeof(word));
hash ^= word;
hash *= FNV_PRIME;
data += 8;
}
// Process remaining bytes
while (data < end) {
hash ^= static_cast<uint64_t>(*data);
hash *= FNV_PRIME;
data++;
}
return hash;
hash = hash * FNV_PRIME;
hash = hash ^ byte;
}
#endif // EAGLE0_BYTEHASHER_HPP
@@ -0,0 +1,173 @@
//
// Created by Dan Crosby on 12/25/20.
//
#ifndef EAGLE0_CONTAINERUTILS_HPP
#define EAGLE0_CONTAINERUTILS_HPP
#include <algorithm>
#include <functional>
#include <optional>
namespace common {
using std::allocator;
using std::back_inserter;
using std::begin;
using std::copy_if;
using std::count_if;
using std::end;
using std::find;
using std::find_if;
using std::function;
using std::optional;
using std::remove_if;
using std::vector;
template<class T, class Container>
auto Contains(const Container& container, const T& elt) -> bool {
return find(begin(container), end(container), elt) != end(container);
}
template<class Container, class Func>
auto CountIf(const Container& container, Func fn) -> size_t {
Container result{};
return count_if(begin(container), end(container), fn);
}
template<class Container, class Func>
void FilterInPlace(Container& container, Func fn) {
container.erase(
remove_if(begin(container), end(container), [fn](const auto& elt) { return !fn(elt); }),
end(container));
}
template<class Container, class Func>
auto Filtered(const Container& container, Func fn) -> Container {
Container result{};
copy_if(begin(container), end(container), back_inserter(result), fn);
return result;
}
template<class Container, class Func>
auto FilteredToVector(const Container& container, Func fn) -> decltype(auto) {
typedef typename Container::value_type value_type;
vector<value_type> result{};
copy_if(begin(container), end(container), back_inserter(result), fn);
return result;
}
template<typename Container, typename Func>
auto FindIf(const Container& container, Func fn) -> optional<typename Container::value_type> {
const auto& t = find_if(begin(container), end(container), fn);
if (t == end(container)) {
return {};
} else {
return optional<typename Container::value_type>(*t);
}
}
template<typename Container, typename Func>
auto ContainsWhere(const Container& container, Func fn) -> bool {
return find_if(begin(container), end(container), fn) != end(container);
}
template<
template<typename, typename>
class TwoTypeContainer,
typename T,
typename Allocator = allocator<T>,
typename Func>
auto Map(const TwoTypeContainer<T, Allocator>& input, Func fn) -> decltype(auto) {
typedef typename decltype(function(fn))::result_type result_type;
TwoTypeContainer<result_type, allocator<result_type>> result{};
result.reserve(input.size());
transform(begin(input), end(input), back_inserter(result), fn);
return result;
}
template<template<typename> class OneTypeContainer, typename T, typename Func>
auto Map(const OneTypeContainer<T>& input, Func fn) -> decltype(auto) {
typedef typename decltype(function(fn))::result_type result_type;
OneTypeContainer<result_type> result{};
result.reserve(input.size());
transform(begin(input), end(input), back_inserter(result), fn);
return result;
}
template<typename Container, typename Func>
auto MapToVector(const Container& input, Func fn) -> decltype(auto) {
typedef typename decltype(function(fn))::result_type result_type;
vector<result_type> result{};
transform(begin(input), end(input), back_inserter(result), fn);
return result;
}
template<
template<typename, typename>
class TwoTypeContainer,
typename T,
typename Allocator = allocator<T>,
typename Func>
auto FlatMap(const TwoTypeContainer<T, Allocator>& input, Func fn) -> decltype(auto) {
typedef typename decltype(function(fn))::result_type::value_type result_value_type;
TwoTypeContainer<result_value_type, allocator<result_value_type>> result{};
for (const auto& elt : input) {
const auto& outContainer = fn(elt);
for (const auto& outElt : outContainer) { result.push_back(outElt); }
}
return result;
}
template<template<typename> class OneTypeContainer, typename T, typename Func>
auto FlatMap(const OneTypeContainer<T>& input, Func fn) -> decltype(auto) {
typedef typename decltype(function(fn))::result_type::value_type result_value_type;
OneTypeContainer<result_value_type> result{};
for (const auto& elt : input) {
const auto& outContainer = fn(elt);
for (const auto& outElt : outContainer) { result.push_back(outElt); }
}
return result;
}
template<typename Container, typename Func>
auto FlatMapToVector(const Container& input, Func fn) -> decltype(auto) {
typedef typename decltype(function(fn))::result_type::value_type value_type;
vector<value_type> result{};
for (const auto& elt : input) {
const auto& outContainer = fn(elt);
for (const auto& outElt : outContainer) { result.push_back(outElt); }
}
return result;
}
template<typename Container>
auto ToVector(const Container& input) -> decltype(auto) {
typedef typename Container::value_type value_type;
return vector<value_type>(begin(input), end(input));
}
template<typename C1, typename C2>
auto Append(C1& recipient, const C2& newItems) -> C1& {
recipient.insert(end(recipient), begin(newItems), end(newItems));
return recipient;
}
} // namespace common
#endif // EAGLE0_CONTAINERUTILS_HPP
@@ -30,7 +30,7 @@ auto rloc(const string& execPath) -> string {
const std::unique_ptr<Runfiles> runfiles(Runfiles::Create(execPath, &error));
if (runfiles == nullptr) {
fprintf(stderr, "Error! %s\n", error.c_str());
printf("Error! %s\n", error.c_str());
abort();
// error handling
}
@@ -67,9 +67,9 @@ auto FilesystemUtils::MapFilesDirectory() -> string {
void FilesystemUtils::MakeDirectoryIfNecessary(const string& directoryPath) {
if (fs::create_directories(directoryPath))
fprintf(stderr, "Directory %s created\n", directoryPath.c_str());
printf("Directory %s created\n", directoryPath.c_str());
else
fprintf(stderr, "No new directory created for %s\n", directoryPath.c_str());
printf("No new directory created for %s\n", directoryPath.c_str());
}
auto FilesystemUtils::SaveFilesDirectory() -> string {
@@ -129,11 +129,11 @@ auto FilesystemUtils::AtomicallySaveToPath(const string& path, const byte_vector
if (ostr.good()) {
const int err = rename(tempPath.c_str(), path.c_str());
if (err == -1) {
fprintf(stderr, "Failed to move file to %s! Errno %d\n", path.c_str(), errno);
printf("Failed to move file to %s! Errno %d\n", path.c_str(), errno);
return false;
}
} else {
fprintf(stderr, "Failed writing to %s!\n", tempPath.c_str());
printf("Failed writing to %s!\n", tempPath.c_str());
return false;
}
@@ -145,7 +145,7 @@ auto FilesystemUtils::LoadFromPath(const string& path) -> byte_vector {
const std::streamsize size = inputFileStream.tellg();
inputFileStream.seekg(0, std::ios::beg);
auto bv = byte_vector(static_cast<size_t>(size));
auto bv = byte_vector(size);
inputFileStream.read((char*)bv.data(), size);
return bv;
@@ -84,9 +84,7 @@ auto RandomGenerator::ChanceOpenEndedPercentileAtOrAbove(const double value) ->
auto StdLibraryGenerator::DoubleZeroToOne() -> double { return unifDouble(engine); }
StdLibraryGenerator::StdLibraryGenerator() : RandomGenerator() {
engine.seed(static_cast<std::mt19937_64::result_type>(std::time(nullptr)));
}
StdLibraryGenerator::StdLibraryGenerator() : RandomGenerator() { engine.seed(std::time(nullptr)); }
auto StdLibraryGenerator::IntBetween(const int min, const int max) -> int {
std::uniform_int_distribution<int> unifInt(min, max - 1);
@@ -14,14 +14,6 @@
#include "src/main/cpp/net/eagle0/common/RandomGenerator.hpp"
// A deterministic random generator that returns values from a fixed sequence.
// Used for testing and MCTS simulation where we want specific, predictable outcomes.
//
// Values in the sequence are treated as [0, 1] probabilities that are returned
// by DoubleZeroToOne(). The normal percentile methods (including open-ended
// variants) work as usual, so callers must provide appropriate sequences.
// For example, to get an open-ended low result of -50, provide [0.02, 0.52]
// which produces: initial=2 (triggers open-ended), accumulated=52, final=2-52=-50
class SequenceRandomGenerator : public ::RandomGenerator {
private:
const std::vector<double> sequence;
@@ -1,14 +0,0 @@
//
// ThreadPool.cpp - Implementation of priority-based thread pool
//
#include "ThreadPool.hpp"
namespace eagle0 {
namespace common {
// Implementation is header-only to support templates
// This file exists for potential future non-template implementations
} // namespace common
} // namespace eagle0
@@ -1,200 +0,0 @@
//
// ThreadPool.hpp - Priority-based thread pool with deadline support
//
#ifndef EAGLE0_THREADPOOL_HPP
#define EAGLE0_THREADPOOL_HPP
#include <atomic>
#include <chrono>
#include <condition_variable>
#include <functional>
#include <future>
#include <memory>
#include <mutex>
#include <queue>
#include <thread>
#include <vector>
namespace eagle0::common {
enum class TaskStatus { SUCCESS = 0, DEADLINE_EXCEEDED = 1, CANCELLED = 2 };
template<typename T>
struct TaskResult {
T value;
TaskStatus status;
TaskResult() : value{}, status(TaskStatus::SUCCESS) {}
TaskResult(T val) : value(std::move(val)), status(TaskStatus::SUCCESS) {}
TaskResult(T val, TaskStatus stat) : value(std::move(val)), status(stat) {}
// NO implicit conversion - this was causing infinite recursion
// Use .value or .get() instead
T get() const { return value; }
bool succeeded() const { return status == TaskStatus::SUCCESS; }
bool deadlineExceeded() const { return status == TaskStatus::DEADLINE_EXCEEDED; }
};
class ThreadPool {
public:
using Clock = std::chrono::steady_clock;
using TimePoint = Clock::time_point;
private:
struct Task {
std::function<void()> function;
int priority;
TimePoint deadline;
bool has_deadline;
Task(std::function<void()> f, int p, TimePoint d, bool has_d)
: function(std::move(f)),
priority(p),
deadline(d),
has_deadline(has_d) {}
// Higher priority values and earlier deadlines have higher priority
bool operator<(const Task& other) const {
if (priority != other.priority) {
return priority < other.priority; // Lower priority values have lower priority in
// priority_queue
}
if (has_deadline && other.has_deadline) {
return deadline > other.deadline; // Later deadlines have lower priority
}
if (has_deadline && !other.has_deadline) {
return false; // Tasks with deadlines have higher priority
}
if (!has_deadline && other.has_deadline) {
return true; // Tasks without deadlines have lower priority
}
return false; // Equal priority, no preference
}
};
std::vector<std::thread> workers;
std::priority_queue<Task> tasks;
std::mutex queue_mutex;
std::condition_variable condition;
std::atomic<bool> stop{false};
public:
explicit ThreadPool(size_t num_threads = std::thread::hardware_concurrency()) {
for (size_t i = 0; i < num_threads; ++i) {
workers.emplace_back([this] {
while (true) {
Task task{nullptr, 0, TimePoint{}, false};
{
std::unique_lock<std::mutex> lock(queue_mutex);
condition.wait(lock, [this] { return stop.load() || !tasks.empty(); });
if (stop.load() && tasks.empty()) { return; }
if (!tasks.empty()) {
task = std::move(const_cast<Task&>(tasks.top()));
tasks.pop();
} else {
continue;
}
}
// Execute the task (deadline checking is now handled inside the task)
if (task.function) { task.function(); }
}
});
}
}
// Enqueue a task with priority only
template<class F, class... Args>
auto enqueue(F&& f, Args&&... args, int priority = 0)
-> std::future<TaskResult<std::invoke_result_t<F, Args...>>> {
using return_type = std::invoke_result_t<F, Args...>;
using result_type = TaskResult<return_type>;
auto actualTask = std::bind(std::forward<F>(f), std::forward<Args>(args)...);
auto task = std::make_shared<std::packaged_task<result_type()>>(
[actualTask = std::move(actualTask)]() mutable -> result_type {
return result_type(actualTask());
});
std::future<result_type> result = task->get_future();
{
std::unique_lock<std::mutex> lock(queue_mutex);
if (stop.load()) { throw std::runtime_error("enqueue on stopped ThreadPool"); }
tasks.emplace([task]() { (*task)(); }, priority, TimePoint{}, false);
}
condition.notify_one();
return result;
}
// Enqueue a task with priority and deadline
template<class F, class... Args>
auto enqueue_with_deadline(F&& f, Args&&... args, int priority, TimePoint deadline)
-> std::future<TaskResult<std::invoke_result_t<F, Args...>>> {
using return_type = std::invoke_result_t<F, Args...>;
using result_type = TaskResult<return_type>;
auto actualTask = std::bind(std::forward<F>(f), std::forward<Args>(args)...);
auto task = std::make_shared<std::packaged_task<result_type()>>(
[actualTask = std::move(actualTask), deadline]() mutable -> result_type {
if (Clock::now() > deadline) {
return result_type(return_type{}, TaskStatus::DEADLINE_EXCEEDED);
}
return result_type(actualTask());
});
std::future<result_type> result = task->get_future();
{
std::unique_lock<std::mutex> lock(queue_mutex);
if (stop.load()) { throw std::runtime_error("enqueue on stopped ThreadPool"); }
tasks.emplace([task]() { (*task)(); }, priority, deadline, true);
}
condition.notify_one();
return result;
}
// Get current queue size (approximate, for monitoring)
size_t queue_size() const {
std::unique_lock<std::mutex> lock(const_cast<std::mutex&>(queue_mutex));
return tasks.size();
}
// Get detailed queue information for debugging
void debug_queue_state() const {
std::unique_lock<std::mutex> lock(const_cast<std::mutex&>(queue_mutex));
printf("ThreadPool: Queue size: %zu\n", tasks.size());
if (!tasks.empty()) {
// Create a copy to inspect priorities without modifying queue
auto queue_copy = tasks;
std::vector<int> priorities;
while (!queue_copy.empty()) {
priorities.push_back(queue_copy.top().priority);
queue_copy.pop();
}
printf("ThreadPool: Priorities in queue: ");
for (int p : priorities) { printf("%d ", p); }
printf("\n");
}
}
~ThreadPool() {
stop.store(true);
condition.notify_all();
for (std::thread& worker : workers) {
if (worker.joinable()) { worker.join(); }
}
}
};
} // namespace eagle0::common
#endif // EAGLE0_THREADPOOL_HPP
@@ -8,8 +8,6 @@ namespace shardok {
using Coords = net::eagle0::shardok::storage::fb::Coords;
constexpr double kDefaultMorale = 50.0;
auto ConvertBattalion(const net::eagle0::common::CommonBattalion &battalion) -> Battalion {
Battalion shardokBattalion{};
@@ -17,9 +15,9 @@ auto ConvertBattalion(const net::eagle0::common::CommonBattalion &battalion) ->
shardokBattalion.mutate_size(battalion.size());
shardokBattalion.mutate_type(
static_cast<net::eagle0::shardok::storage::fb::BattalionTypeId>(battalion.type()));
shardokBattalion.mutate_morale(kDefaultMorale);
shardokBattalion.mutate_armament(static_cast<float>(battalion.armament()));
shardokBattalion.mutate_training(static_cast<float>(battalion.training()));
shardokBattalion.mutate_morale(battalion.morale());
shardokBattalion.mutate_armament(battalion.armament());
shardokBattalion.mutate_training(battalion.training());
return shardokBattalion;
}
@@ -39,28 +37,28 @@ auto ConvertHero(const net::eagle0::common::CommonHero &hero) -> Hero {
shardokHero.mutable_control_info().mutate_controlled_unit_id(-1);
shardokHero.mutable_control_info().mutate_controlled_this_round(false);
shardokHero.mutate_strength(static_cast<int8_t>(hero.strength()));
shardokHero.mutate_strength_xp(static_cast<int16_t>(hero.strength_xp()));
shardokHero.mutate_strength(hero.strength());
shardokHero.mutate_strength_xp(hero.strength_xp());
shardokHero.mutate_agility(static_cast<int8_t>(hero.agility()));
shardokHero.mutate_agility_xp(static_cast<int16_t>(hero.agility_xp()));
shardokHero.mutate_agility(hero.agility());
shardokHero.mutate_agility_xp(hero.agility_xp());
shardokHero.mutate_constitution(static_cast<int8_t>(hero.constitution()));
shardokHero.mutate_constitution_xp(static_cast<int16_t>(hero.constitution_xp()));
shardokHero.mutate_constitution(hero.constitution());
shardokHero.mutate_constitution_xp(hero.constitution_xp());
shardokHero.mutate_charisma(static_cast<int8_t>(hero.charisma()));
shardokHero.mutate_charisma_xp(static_cast<int16_t>(hero.charisma_xp()));
shardokHero.mutate_charisma(hero.charisma());
shardokHero.mutate_charisma_xp(hero.charisma_xp());
shardokHero.mutate_wisdom(static_cast<int8_t>(hero.wisdom()));
shardokHero.mutate_wisdom_xp(static_cast<int16_t>(hero.wisdom_xp()));
shardokHero.mutate_wisdom(hero.wisdom());
shardokHero.mutate_wisdom_xp(hero.wisdom_xp());
shardokHero.mutate_integrity(static_cast<int8_t>(hero.integrity()));
shardokHero.mutate_ambition(static_cast<int8_t>(hero.ambition()));
shardokHero.mutate_gregariousness(static_cast<int8_t>(hero.gregariousness()));
shardokHero.mutate_bravery(static_cast<int8_t>(hero.bravery()));
shardokHero.mutate_integrity(hero.integrity());
shardokHero.mutate_ambition(hero.ambition());
shardokHero.mutate_gregariousness(hero.gregariousness());
shardokHero.mutate_bravery(hero.bravery());
shardokHero.mutate_vigor(static_cast<float>(hero.vigor()));
shardokHero.mutate_starting_vigor(static_cast<float>(hero.vigor()));
shardokHero.mutate_vigor(hero.vigor());
shardokHero.mutate_starting_vigor(hero.vigor());
return shardokHero;
}
@@ -95,22 +93,19 @@ auto ConvertUnit(
shardokUnit.mutate_stun_rounds_remaining(0);
for (const PlayerId pid : allPlayerIds) {
shardokUnit.mutable_opponent_knowledge()->Mutate(
static_cast<flatbuffers::uoffset_t>(pid),
0);
shardokUnit.mutable_opponent_knowledge()->Mutate(pid, 0);
}
shardokUnit.mutate_has_moved_in_zoc(false);
shardokUnit.mutate_targeted_unit(-1);
shardokUnit.mutate_volleys_remaining(0);
shardokUnit.mutate_food_remaining(static_cast<float>(unit.food()));
shardokUnit.mutate_food_remaining(unit.food());
shardokUnit.mutate_can_flee(unit.can_flee());
shardokUnit.mutate_can_archery(unit.can_archery());
shardokUnit.mutate_can_start_fire(unit.can_start_fire());
if (unit.has_starting_position_index()) {
shardokUnit.mutate_starting_position_index(
static_cast<int8_t>(unit.starting_position_index().value()));
shardokUnit.mutate_starting_position_index(unit.starting_position_index().value());
} else {
shardokUnit.mutate_starting_position_index(-1);
}
@@ -9,10 +9,7 @@
#include "src/main/cpp/net/eagle0/shardok/library/ShardokCTypes.h"
#include "src/main/flatbuffer/net/eagle0/shardok/storage/unit.hpp"
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wdeprecated-redundant-constexpr-static-def"
#include "src/main/protobuf/net/eagle0/common/common_unit.pb.h"
#pragma GCC diagnostic pop
namespace shardok {
@@ -1,139 +0,0 @@
# MCTS (Monte Carlo Tree Search) Framework
This directory contains a game-agnostic Monte Carlo Tree Search implementation that can be used with any turn-based game. The framework separates the MCTS algorithm from game-specific logic through abstract interfaces.
## Core Abstract Classes
### `MCTSAction` (abstract/MCTSAction.hpp)
Abstract interface for representing game actions/moves.
**Key Methods:**
- `getIndex()` - Returns the action's unique identifier
- `getDescription()` - Human-readable description for debugging/logging
- `clone()` - Creates a deep copy of the action
- `equals()` - Compares actions for equality
### `MCTSGameState` (abstract/MCTSGameState.hpp)
Abstract interface for representing game states.
**Key Methods:**
- `hash()` - Returns a hash for transposition table lookups
- `score(playerId)` - Evaluates the state's value for a given player
- `currentPlayerId()` - Returns whose turn it is
- `isTerminal()` - Checks if the game has ended
- `getWinner()` - Returns the winning player (if terminal)
- `clone()` - Creates a deep copy of the state
- `equals()` - Compares states for equality
### `MCTSGameEngine` (abstract/MCTSGameEngine.hpp)
Abstract interface for game rule enforcement and state transitions. Many methods have efficient default implementations.
**Must Override (Pure Virtual):**
- `applyAction(state, action)` - Applies an action to create a new state
- `getLegalActions(state)` - Returns all valid moves from a state
- `isTerminal(state)` - Checks if a state is game-ending
- `evaluateState(state, playerId)` - Scores a state for a player
**Optional Overrides (Have Default Implementations):**
- `applyActionMutable(state, action)` - Apply action in-place for efficiency (default: calls applyAction)
- `filterActions(actions, state)` - Applies heuristic filtering (default: no filtering)
- `simulateRandomPlayout(state, playerId, maxDepth, policy)` - Runs simulation (default: efficient mutable implementation)
- `getActionScore(state, action, playerId)` - Scores an action (default: apply and evaluate)
- `shouldStopSearch(state, iterations, startTime)` - Early termination (default: no early stop)
**Performance Features:**
- The default `simulateRandomPlayout` clones the state once and mutates it throughout simulation for efficiency
- Games can override `applyActionMutable` to provide even more efficient in-place updates
- Games can override `simulateRandomPlayout` for custom optimizations (e.g., using internal engine state)
## MCTS Algorithm Implementation
### `AbstractMCTSAI` (abstract/AbstractMCTSAI.hpp)
The main MCTS algorithm implementation that works with any game implementing the abstract interfaces.
**Key Features:**
- **Selection**: Uses UCB1 (Upper Confidence Bound) for node selection
- **Expansion**: Adds new nodes to the search tree
- **Simulation**: Runs random playouts to estimate node values
- **Backpropagation**: Updates node statistics with simulation results
- **Multithreading**: Supports parallel MCTS with configurable thread count
- **Path Compression**: Optimizes move sequences for better performance
**Configuration Options:**
- `explorationConstant` - UCB1 exploration parameter (default: √2)
- `maxSimulationDepth` - Maximum depth for random playouts
- `maxTreeDepth` - Maximum tree depth to prevent stack overflow
- `useMultithreading` - Enable parallel search
- `numThreads` - Number of worker threads
- `simulationPolicy` - Strategy for action selection during simulation
### `MCTSNode` (abstract/MCTSNode.hpp)
Represents nodes in the MCTS search tree.
**Core Data:**
- `action` - The action that led to this node
- `actionIndex` - Index in the original actions array
- `gameState` - The game state at this node
- `visitCount` - Number of times this node was visited
- `totalReward` - Sum of simulation rewards
- `averageReward` - Average reward (totalReward / visitCount)
- `children` - Child nodes in the search tree
- `parent` - Parent node reference
**Key Methods:**
- `CanExpand()` - Checks if node has untried actions
- `GetBestChild(explorationConstant)` - UCB1-based child selection
- `GetBestFinalChild()` - Most-visited child (for final move selection)
- `CalculateUCB1(explorationConstant)` - Computes UCB1 value
## Simulation Policies
The framework supports multiple strategies for action selection during random playouts:
- **RANDOM** - Uniform random selection
- **FILTERED_RANDOM** - Random selection from filtered action set
- **BEST_IMMEDIATE** - Always choose the highest-scoring immediate action
- **WEIGHTED_BEST_IMMEDIATE** - Weighted random selection based on action scores
## Type Definitions
### `MCTSTypes` (abstract/MCTSTypes.hpp)
- `MCTSPlayerId` - Player identifier type (int)
- `MCTSSimulationPolicy` - Enumeration of simulation strategies
- `MCTSConfig` - Configuration structure for MCTS parameters
## Usage Pattern
To use this framework with your game:
1. **Implement the abstract interfaces** for your game:
```cpp
class MyGameAction : public MCTSAction { /* ... */ };
class MyGameState : public MCTSGameState { /* ... */ };
class MyGameEngine : public MCTSGameEngine { /* ... */ };
```
2. **Create and configure the AI**:
```cpp
MCTSConfig config;
config.explorationConstant = 1.414;
config.maxSimulationDepth = 100;
AbstractMCTSAI ai(playerId, config);
```
3. **Run the search**:
```cpp
auto actions = engine.getLegalActions(currentState);
auto result = ai.Search(engine, currentState, actions, timeLimit);
auto bestAction = actions[result.bestActionIndex];
```
## Testing
The framework includes comprehensive tests using a Tic-Tac-Toe implementation:
- `MockTicTacToe.hpp` - Example implementation of all abstract interfaces
- `AbstractMCTSAI_test.cpp` - Unit tests for the core algorithm
- `MCTSIntegration_test.cpp` - Integration tests with complete games
- `MCTSNode_test.cpp` - Tests for the node data structure
This demonstrates how to implement the interfaces and validates that the MCTS algorithm works correctly with any turn-based game.
File diff suppressed because it is too large Load Diff
@@ -1,106 +0,0 @@
//
// Abstract MCTS AI implementation - game agnostic
//
#ifndef EAGLE0_ABSTRACT_MCTSAI_HPP
#define EAGLE0_ABSTRACT_MCTSAI_HPP
#include <chrono>
#include <memory>
#include <unordered_map>
#include <vector>
#include "MCTSAction.hpp"
#include "MCTSGameEngine.hpp"
#include "MCTSGameState.hpp"
#include "MCTSNode.hpp"
#include "MCTSTypes.hpp"
namespace shardok {
namespace mcts {
class AbstractMCTSAI {
public:
// Search result structure
struct SearchResult {
size_t bestActionIndex = 0;
double bestScore = 0.0;
int searchDepth = 0;
int nodesEvaluated = 0;
std::chrono::milliseconds searchTime{0};
bool foundWinningMove = false;
};
explicit AbstractMCTSAI(MCTSPlayerId playerId, MCTSConfig config = MCTSConfig{});
// Main search interface
[[nodiscard]] auto Search(
const MCTSGameEngine& engine,
const MCTSGameState& initialState,
std::chrono::milliseconds timeLimit) const -> SearchResult;
// Configuration
[[nodiscard]] auto GetConfig() const -> const MCTSConfig& { return config_; }
void SetConfig(const MCTSConfig& newConfig) { config_ = newConfig; }
[[nodiscard]] auto FindNodeAtDepthWithHash(
const MCTSNode* root,
int maxDepth,
uint64_t targetHash) -> const MCTSNode*;
private:
MCTSPlayerId playerId_;
MCTSConfig config_;
// Transposition table: maps state hash -> minimum depth at which state was reached
// Used to detect and penalize longer paths to the same game state
// Cleared at the start of each Search() call
mutable std::unordered_map<uint64_t, int> transpositionTable_;
// Core MCTS algorithm
[[nodiscard]] auto BuildMCTSTree(
const MCTSGameEngine& engine,
const MCTSGameState& initialState,
std::chrono::steady_clock::time_point deadline) const -> std::unique_ptr<MCTSNode>;
// MCTS phases
[[nodiscard]] auto MCTSSelection(MCTSNode* root) const -> MCTSNode*;
[[nodiscard]] auto MCTSExpansion(MCTSNode* node, const MCTSGameEngine& engine) const
-> MCTSNode*;
[[nodiscard]] auto MCTSSimulation(
const MCTSGameEngine& engine,
const MCTSGameState& state,
MCTSPlayerId startingPlayer,
int startingPlayerFlips = 0) const -> double;
auto MCTSBackpropagation(MCTSNode* node, double reward, MCTSBackpropagationPolicy policy) const
-> void;
// Helper functions
[[nodiscard]] auto SelectSimulationAction(
const MCTSGameEngine& engine,
const MCTSGameState& state,
const std::vector<std::unique_ptr<MCTSAction>>& actions,
bool isMaximizing) const -> size_t;
// Logging
static auto LogSearchResults(
const MCTSNode* rootNode,
const MCTSNode* bestChild,
const SearchResult& result) -> void;
// Debug tree dumping
static auto DumpTreeToFile(const MCTSNode* root, const std::string& filepath) -> void;
private:
static auto
DumpNodeRecursive(const MCTSNode* node, std::ostream& out, int indentLevel, bool isLastChild)
-> void;
};
} // namespace mcts
} // namespace shardok
#endif // EAGLE0_ABSTRACT_MCTSAI_HPP
@@ -1,94 +0,0 @@
load("//tools:copts.bzl", "COPTS")
cc_library(
name = "mcts_types",
hdrs = ["MCTSTypes.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai/mcts:__subpackages__",
"//src/test/cpp/net/eagle0/common/mcts:__subpackages__",
"//src/test/cpp/net/eagle0/shardok/ai/mcts:__subpackages__",
],
)
cc_library(
name = "mcts_action",
hdrs = ["MCTSAction.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai/mcts:__subpackages__",
"//src/test/cpp/net/eagle0/common/mcts:__subpackages__",
"//src/test/cpp/net/eagle0/shardok/ai/mcts:__subpackages__",
],
)
cc_library(
name = "mcts_game_state",
hdrs = ["MCTSGameState.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai/mcts:__subpackages__",
"//src/test/cpp/net/eagle0/common/mcts:__subpackages__",
"//src/test/cpp/net/eagle0/shardok/ai/mcts:__subpackages__",
],
deps = [
":mcts_types",
],
)
cc_library(
name = "mcts_game_engine",
srcs = ["MCTSGameEngine.cpp"],
hdrs = ["MCTSGameEngine.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai/mcts:__subpackages__",
"//src/test/cpp/net/eagle0/common/mcts:__subpackages__",
"//src/test/cpp/net/eagle0/shardok/ai/mcts:__subpackages__",
],
deps = [
":mcts_action",
":mcts_game_state",
":mcts_types",
],
)
cc_library(
name = "mcts_node",
hdrs = ["MCTSNode.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai:__pkg__",
"//src/main/cpp/net/eagle0/shardok/ai/mcts:__subpackages__",
"//src/test/cpp/net/eagle0/common/mcts:__subpackages__",
"//src/test/cpp/net/eagle0/shardok/ai/mcts:__subpackages__",
],
deps = [
":mcts_action",
":mcts_game_state",
":mcts_types",
],
)
cc_library(
name = "abstract_mcts_ai",
srcs = ["AbstractMCTSAI.cpp"],
hdrs = ["AbstractMCTSAI.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai/mcts:__subpackages__",
"//src/test/cpp/net/eagle0/common/mcts:__subpackages__",
"//src/test/cpp/net/eagle0/shardok/ai/mcts:__subpackages__",
],
deps = [
":mcts_action",
":mcts_game_engine",
":mcts_game_state",
":mcts_node",
":mcts_types",
"//src/main/cpp/net/eagle0/common/mcts/util:tree_indent_util",
],
)
# Individual targets are exposed above - no need for a catch-all target
# Each component should be imported explicitly by its consumers
@@ -1,40 +0,0 @@
//
// Abstract action interface for MCTS
//
#ifndef EAGLE0_MCTS_ACTION_HPP
#define EAGLE0_MCTS_ACTION_HPP
#include <memory>
#include <string>
namespace shardok {
namespace mcts {
// Abstract interface for game actions
class MCTSAction {
public:
virtual ~MCTSAction() = default;
// Get a unique index for this action (used for command indexing)
[[nodiscard]] virtual size_t getIndex() const = 0;
// Get a human-readable description for debugging/logging
[[nodiscard]] virtual std::string getDescription() const = 0;
// Create a deep copy of this action
[[nodiscard]] virtual std::unique_ptr<MCTSAction> clone() const = 0;
// Check if two actions are equivalent
[[nodiscard]] virtual bool equals(const MCTSAction& other) const = 0;
// Check if this action requires a chance node (binary success/failure outcome)
// Examples: START_FIRE, RAISE_DEAD, EXTINGUISH_FIRE
// If true, the game engine should provide outcome probabilities
[[nodiscard]] virtual bool requiresChanceNode() const = 0;
};
} // namespace mcts
} // namespace shardok
#endif // EAGLE0_MCTS_ACTION_HPP
@@ -1,148 +0,0 @@
//
// Default implementations for MCTSGameEngine
//
#include "MCTSGameEngine.hpp"
#include <algorithm>
#include <limits>
#include <random>
#include <vector>
#include "MCTSTypes.hpp" // For MCTSInternalError
namespace shardok {
namespace mcts {
double MCTSGameEngine::simulateRandomPlayout(
const MCTSGameState& state,
MCTSPlayerId playerId,
int maxDepth,
MCTSSimulationPolicy policy) const {
// Clone state once and mutate it throughout simulation for efficiency
auto currentState = state.clone();
int depth = 0;
// Use thread-local random generator for thread safety
static thread_local std::mt19937 gen(std::random_device{}());
// Simulate until terminal or max depth
while (!currentState->isTerminal() && depth < maxDepth) {
auto actions = getLegalActions(*currentState, playerId, 0, 0);
if (actions.empty()) { break; }
size_t selectedIndex = 0;
// Select action based on policy
switch (policy) {
case MCTSSimulationPolicy::RANDOM: {
std::uniform_int_distribution<> dis(0, actions.size() - 1);
selectedIndex = dis(gen);
break;
}
case MCTSSimulationPolicy::FILTERED_RANDOM: {
auto filteredIndices = filterActions(actions, *currentState);
if (!filteredIndices.empty()) {
std::uniform_int_distribution<> dis(0, filteredIndices.size() - 1);
selectedIndex = filteredIndices[dis(gen)];
} else {
// Fall back to random if no actions pass filter
std::uniform_int_distribution<> dis(0, actions.size() - 1);
selectedIndex = dis(gen);
}
break;
}
case MCTSSimulationPolicy::BEST_IMMEDIATE: {
double bestScore = -std::numeric_limits<double>::infinity();
for (size_t i = 0; i < actions.size(); ++i) {
double score = getActionScore(
*currentState,
*actions[i],
currentState->currentPlayerId());
if (score > bestScore) {
bestScore = score;
selectedIndex = i;
}
}
break;
}
case MCTSSimulationPolicy::WEIGHTED_BEST_IMMEDIATE: {
// Score all actions and weight by ranking
std::vector<std::pair<size_t, double>> scores;
scores.reserve(actions.size());
for (size_t i = 0; i < actions.size(); ++i) {
double score = getActionScore(
*currentState,
*actions[i],
currentState->currentPlayerId());
scores.emplace_back(i, score);
}
// Sort by score (descending)
std::sort(scores.begin(), scores.end(), [](const auto& a, const auto& b) {
return a.second > b.second;
});
// Create weights based on ranking (1/rank)
std::vector<double> weights;
weights.reserve(scores.size());
for (size_t i = 0; i < scores.size(); ++i) { weights.push_back(1.0 / (i + 1.0)); }
// Select based on weights
std::discrete_distribution<> dis(weights.begin(), weights.end());
selectedIndex = scores[dis(gen)].first;
break;
}
case MCTSSimulationPolicy::WEIGHTED_HEURISTIC: {
// Get heuristic weights (fast O(1) per action)
const auto weights = getActionWeights(actions, *currentState);
// Filter out zero-weight actions
std::vector<size_t> validIndices;
std::vector<double> validWeights;
validIndices.reserve(actions.size());
validWeights.reserve(actions.size());
for (size_t i = 0; i < weights.size() && i < actions.size(); ++i) {
if (weights[i] > 0.0) {
validIndices.push_back(i);
validWeights.push_back(weights[i]);
}
}
// If all actions filtered out, this is a bug in the weighting logic
if (validWeights.empty()) {
throw MCTSInternalError(
"MCTS simulation (playout): All actions have zero weight in "
"WEIGHTED_HEURISTIC policy (action count: " +
std::to_string(actions.size()) +
") - this indicates incorrect weighting");
}
// Select based on heuristic weights
std::discrete_distribution<> dis(validWeights.begin(), validWeights.end());
selectedIndex = validIndices[dis(gen)];
break;
}
}
// Apply selected action using mutable version for efficiency
applyActionMutable(currentState, *actions[selectedIndex]);
if (!currentState) {
break; // Failed to apply action
}
depth++;
}
// Return evaluation from original player's perspective
return evaluateState(*currentState, playerId);
}
} // namespace mcts
} // namespace shardok
@@ -1,161 +0,0 @@
//
// Abstract game engine interface for MCTS
//
#ifndef EAGLE0_MCTS_GAME_ENGINE_HPP
#define EAGLE0_MCTS_GAME_ENGINE_HPP
#include <chrono>
#include <memory>
#include <vector>
#include "MCTSAction.hpp"
#include "MCTSGameState.hpp"
#include "MCTSTypes.hpp"
namespace shardok {
namespace mcts {
// Information about chance outcomes (supports both binary and multi-outcome)
struct ChanceOutcomeInfo {
std::vector<double> probabilities; // Probability of each outcome (must sum to 1.0)
std::vector<double> rolls; // Roll values for each outcome
// Factory for binary success/failure outcomes (e.g., START_FIRE)
[[nodiscard]] static ChanceOutcomeInfo binary(double successProbability) {
// -100: triggers open-ended low sequence, succeeds against any threshold
// 150: triggers open-ended high sequence, fails against any threshold
return {{successProbability, 1.0 - successProbability}, {-100.0, 150.0}};
}
// Factory for multi-outcome with fixed seeds (e.g., END_TURN)
// Uses uniformly distributed roll values to sample different random outcomes
[[nodiscard]] static ChanceOutcomeInfo multiOutcome(int numOutcomes) {
std::vector<double> probs(numOutcomes, 1.0 / numOutcomes);
std::vector<double> rollValues;
rollValues.reserve(numOutcomes);
// Spread rolls across the percentile range: 10, 30, 50, 70, 90 for 5 outcomes
for (int i = 0; i < numOutcomes; ++i) {
rollValues.push_back(10.0 + (80.0 * i) / (numOutcomes - 1));
}
return {probs, rollValues};
}
[[nodiscard]] const std::vector<double>& getRepresentativeRolls() const { return rolls; }
[[nodiscard]] const std::vector<double>& getProbabilities() const { return probabilities; }
};
// Backward compatibility alias
using BinaryOutcomeInfo = ChanceOutcomeInfo;
// Abstract interface for game engines
class MCTSGameEngine {
public:
virtual ~MCTSGameEngine() = default;
// Apply an action to a state and return the resulting state
// If deterministicRoll is provided (0.0-100.0), use that for any random outcomes
[[nodiscard]] virtual std::unique_ptr<MCTSGameState> applyAction(
const MCTSGameState& state,
const MCTSAction& action,
double deterministicRoll = -1.0) const = 0;
// Apply an action to a mutable state in-place (for efficient simulation)
// Default: clone, apply, and move the result back
// Override this for better performance
virtual void applyActionMutable(std::unique_ptr<MCTSGameState>& state, const MCTSAction& action)
const {
state = applyAction(*state, action);
}
// Get all legal actions for the current state with player flip tracking
// Default implementation ignores flip tracking and calls base version
[[nodiscard]] virtual std::vector<std::unique_ptr<MCTSAction>> getLegalActions(
const MCTSGameState& state,
MCTSPlayerId /*rootPlayerId*/,
int /*currentPlayerFlips*/,
int /*maxPlayerFlips*/) const = 0;
// Check if a state is terminal
[[nodiscard]] virtual bool isTerminal(const MCTSGameState& state) const = 0;
// Evaluate a state from the perspective of a player
[[nodiscard]] virtual double evaluateState(const MCTSGameState& state, MCTSPlayerId playerId)
const = 0;
// Filter actions based on game-specific heuristics
// Returns indices of actions to keep
// Default: no filtering (return all indices)
[[nodiscard]] virtual std::vector<size_t> filterActions(
const std::vector<std::unique_ptr<MCTSAction>>& actions,
const MCTSGameState& /*state*/) const {
std::vector<size_t> indices;
indices.reserve(actions.size());
for (size_t i = 0; i < actions.size(); ++i) { indices.push_back(i); }
return indices;
}
// Get heuristic weights for actions (used by WEIGHTED_HEURISTIC simulation policy)
// Returns weights corresponding to each action (same size as actions vector)
// Weight of 0.0 = never select, higher = more likely to select
// Default: uniform weights (all actions equally likely)
[[nodiscard]] virtual std::vector<double> getActionWeights(
const std::vector<std::unique_ptr<MCTSAction>>& actions,
const MCTSGameState& /*state*/) const {
// Default: uniform weights
return std::vector<double>(actions.size(), 1.0);
}
// Simulate a random playout from the given state
// Default implementation uses policy to select actions
[[nodiscard]] virtual double simulateRandomPlayout(
const MCTSGameState& state,
MCTSPlayerId playerId,
int maxDepth,
MCTSSimulationPolicy policy) const;
// Get the immediate score of applying an action
// Default: apply the action and evaluate the resulting state
[[nodiscard]] virtual double getActionScore(
const MCTSGameState& state,
const MCTSAction& action,
MCTSPlayerId playerId) const {
auto newState = applyAction(state, action);
if (!newState) { return 0.0; }
return evaluateState(*newState, playerId);
}
// Check if we should stop searching (e.g., time limit, found winning move)
[[nodiscard]] virtual bool shouldStopSearch(
const MCTSGameState& /*state*/,
int /*iterations*/,
std::chrono::steady_clock::time_point /*startTime*/) const {
// Default: no early stopping
return false;
}
// Map a filtered action index back to the original unfiltered index
// This is needed when getLegalActions() applies filtering - the returned actions
// may be a subset of all available actions, and this maps back to the original index.
// Default implementation: no filtering, so filtered index = original index
[[nodiscard]] virtual size_t mapFilteredIndexToOriginal(
size_t filteredIndex,
const MCTSGameState& state) const {
// Default: no filtering, index stays the same
(void)state; // Suppress unused parameter warning
return filteredIndex;
}
// Get binary outcome information for an action that requires a chance node
// Only called for actions where action.requiresChanceNode() returns true
// Returns success probability for binary success/failure actions
[[nodiscard]] virtual BinaryOutcomeInfo getBinaryOutcomeInfo(
const MCTSGameState& state,
const MCTSAction& action) const = 0;
};
} // namespace mcts
} // namespace shardok
#endif // EAGLE0_MCTS_GAME_ENGINE_HPP
@@ -1,50 +0,0 @@
//
// Abstract game state interface for MCTS
//
#ifndef EAGLE0_MCTS_GAME_STATE_HPP
#define EAGLE0_MCTS_GAME_STATE_HPP
#include <cstdint>
#include <memory>
#include <string>
#include "MCTSTypes.hpp"
namespace shardok {
namespace mcts {
// Abstract interface for game states
class MCTSGameState {
public:
virtual ~MCTSGameState() = default;
// Compute hash for transposition table
[[nodiscard]] virtual uint64_t hash() const = 0;
// Evaluate the state from the perspective of the given player
[[nodiscard]] virtual double score(MCTSPlayerId playerId) const = 0;
// Get the player whose turn it is
[[nodiscard]] virtual MCTSPlayerId currentPlayerId() const = 0;
// Check if the game has ended
[[nodiscard]] virtual bool isTerminal() const = 0;
// Create a deep copy of the state
[[nodiscard]] virtual std::unique_ptr<MCTSGameState> clone() const = 0;
// Check if two states are equivalent
[[nodiscard]] virtual bool equals(const MCTSGameState& other) const = 0;
// Get winner if terminal, or -1 if not terminal or draw
[[nodiscard]] virtual MCTSPlayerId getWinner() const = 0;
// Optional: Get a string representation for debugging
[[nodiscard]] virtual std::string toString() const { return "MCTSGameState"; }
};
} // namespace mcts
} // namespace shardok
#endif // EAGLE0_MCTS_GAME_STATE_HPP
@@ -1,277 +0,0 @@
//
// Abstract MCTS Node structure for game-agnostic implementation
//
#ifndef EAGLE0_ABSTRACT_MCTSNODE_HPP
#define EAGLE0_ABSTRACT_MCTSNODE_HPP
#include <cmath>
#include <limits>
#include <memory>
#include <vector>
#include "MCTSAction.hpp"
#include "MCTSGameState.hpp"
#include "MCTSTypes.hpp"
namespace shardok {
namespace mcts {
// Node type for MCTS tree
enum class NodeType {
DECISION, // Player chooses an action (standard MCTS node)
CHANCE // Nature determines outcome (for probabilistic actions)
};
// Abstract MCTS Node structure
struct MCTSNode {
// Node type
NodeType nodeType = NodeType::DECISION;
// Action information
std::unique_ptr<MCTSAction> action; // The action that led to this node (null for root)
size_t actionIndex = SIZE_MAX; // Index in the original actions array (SIZE_MAX for root)
// Score information
double immediateScore = 0.0;
double lookaheadScore = 0.0;
// Game state after this action
std::unique_ptr<MCTSGameState> gameState;
// MCTS statistics
int visitCount = 0;
double totalReward = 0.0;
double averageReward = 0.0;
mutable double ucb1Value = 0.0;
double actionWeight = 1.0; // Prior probability/weight for this action (from heuristics)
// Tree structure
std::vector<std::unique_ptr<MCTSNode>> children;
size_t nextUntriedActionIndex = 0; // Next action to expand
size_t totalActions = 0; // Total number of available actions
MCTSNode* parent = nullptr;
// Chance node specific fields (only used when nodeType == CHANCE)
std::vector<double> outcomeProbabilities; // Probability of each outcome
std::vector<double> outcomeRolls; // Representative roll for each outcome
// Game context
MCTSPlayerId playerId;
int depth = 0;
bool isTerminal = false;
int playerFlips = 0; // Number of times the active player has changed from root player
bool isMaximizingPlayer = true; // True if this node is maximizing for root player
// Transposition detection
uint64_t stateHash = 0;
bool isRedundant = false; // True if this node represents a duplicate state
// Constructor for root node
MCTSNode(std::unique_ptr<MCTSGameState> state, MCTSPlayerId pid, int d)
: gameState(std::move(state)),
playerId(pid),
depth(d),
playerFlips(0),
isMaximizingPlayer(true) {
if (gameState) {
stateHash = gameState->hash();
isTerminal = gameState->isTerminal();
}
}
// Constructor for child node
MCTSNode(
std::unique_ptr<MCTSAction> act,
std::unique_ptr<MCTSGameState> state,
MCTSPlayerId pid,
int d,
size_t actIdx = SIZE_MAX,
int flips = 0,
bool isMaximizing = true,
double weight = 1.0)
: action(std::move(act)),
actionIndex(actIdx),
gameState(std::move(state)),
actionWeight(weight),
playerId(pid),
depth(d),
playerFlips(flips),
isMaximizingPlayer(isMaximizing) {
if (gameState) {
stateHash = gameState->hash();
isTerminal = gameState->isTerminal();
}
}
// Iterative destructor to avoid stack overflow with deep trees
~MCTSNode() {
std::vector<std::unique_ptr<MCTSNode>> nodesToDestroy;
nodesToDestroy.swap(children);
while (!nodesToDestroy.empty()) {
std::vector<std::unique_ptr<MCTSNode>> currentBatch;
currentBatch.swap(nodesToDestroy);
for (const auto& node : currentBatch) {
if (node && !node->children.empty()) {
for (auto& child : node->children) {
nodesToDestroy.push_back(std::move(child));
}
node->children.clear();
}
}
}
}
// Calculate UCB1 value for this node from parent's perspective
// Uses prior-weighted formula similar to AlphaGo:
// UCB = Q + c * P * sqrt(N_parent) / (1 + N_child)
// Where P is the action weight (prior probability from heuristics)
[[nodiscard]] double CalculateUCB1(
const double explorationConstant,
const int parentVisitCount,
const bool parentIsMaximizing) const {
// Exploitation: use lookahead score (minimax value)
// For minimizing nodes, negate the score to prefer low child values
const double exploitationValue = parentIsMaximizing ? lookaheadScore : -lookaheadScore;
// Exploration: prior-weighted formula (AlphaGo-style)
// Actions with weight 0.0 (like FLEE_COMMAND) get no exploration bonus
// Unvisited nodes get: c * weight * sqrt(N_parent)
// This prevents bad actions from dominating exploration due to infinite UCB
const double explorationValue = explorationConstant * actionWeight *
std::sqrt(parentVisitCount) / (1.0 + visitCount);
return exploitationValue + explorationValue;
}
// Check if this node can be expanded
[[nodiscard]] bool CanExpand() const { return nextUntriedActionIndex < totalActions; }
// Check if this is a chance node
[[nodiscard]] bool IsChanceNode() const { return nodeType == NodeType::CHANCE; }
// Check if this is a decision node
[[nodiscard]] bool IsDecisionNode() const { return nodeType == NodeType::DECISION; }
// Get best child from chance node (probability-weighted selection)
// For chance nodes, we want to explore outcomes proportionally to their probability
[[nodiscard]] MCTSNode* GetBestChanceChild() const {
if (children.empty() || !IsChanceNode()) return nullptr;
// Find the outcome that is most under-explored relative to its probability
// Expected visits for outcome i: total_visits * probability[i]
// Actual visits: child[i]->visitCount
// Deficit: expected - actual
size_t bestIndex = 0;
double bestDeficit = -std::numeric_limits<double>::max();
for (size_t i = 0; i < children.size(); i++) {
if (!children[i] || children[i]->isRedundant) continue;
const double expectedVisits = visitCount * outcomeProbabilities[i];
const double actualVisits = static_cast<double>(children[i]->visitCount);
const double deficit = expectedVisits - actualVisits;
if (deficit > bestDeficit) {
bestDeficit = deficit;
bestIndex = i;
}
}
return children[bestIndex].get();
}
// Get best child based on UCB1
[[nodiscard]] MCTSNode* GetBestChild(const double explorationConstant) const {
if (children.empty()) return nullptr;
MCTSNode* bestChild = nullptr;
double bestValue = -std::numeric_limits<double>::max();
for (auto& child : children) {
// Skip redundant nodes
if (child->isRedundant) continue;
// Calculate UCB1 value using the helper function
const double value =
child->CalculateUCB1(explorationConstant, visitCount, isMaximizingPlayer);
// Debug logging for UCB selection
static bool enableUCBDebug = false;
if (enableUCBDebug && child->visitCount > 0) {
const double exploitationValue =
isMaximizingPlayer ? child->lookaheadScore : -child->lookaheadScore;
const double explorationValue =
explorationConstant * std::sqrt(std::log(visitCount) / child->visitCount);
printf(" UCB: %s lookahead=%.2f expl=%.2f (+%.2f) = %.2f [%s]\n",
isMaximizingPlayer ? "MAX" : "MIN",
child->lookaheadScore,
exploitationValue,
explorationValue,
value,
child->action ? child->action->getDescription().c_str() : "root");
}
if (value > bestValue) {
bestValue = value;
bestChild = child.get();
}
}
return bestChild;
}
// Get best child based on visit count (for final selection)
[[nodiscard]] MCTSNode* GetBestFinalChild() const {
if (children.empty()) return nullptr;
MCTSNode* bestChild = nullptr;
int bestVisits = 0;
double bestScore = isMaximizingPlayer ? -std::numeric_limits<double>::max()
: std::numeric_limits<double>::max();
for (const auto& child : children) {
// Skip redundant nodes
if (child->isRedundant) continue;
// Prefer most-visited node (robust child selection)
if (child->visitCount > bestVisits) {
bestVisits = child->visitCount;
bestScore = child->lookaheadScore;
bestChild = child.get();
} else if (child->visitCount == bestVisits) {
// Tie-break on lookahead score (minimax value, not poisoned average)
// Maximizing: prefer higher score (better for root player)
// Minimizing: prefer lower score (worse for root player)
const bool shouldReplace = isMaximizingPlayer ? (child->lookaheadScore > bestScore)
: (child->lookaheadScore < bestScore);
if (shouldReplace) {
bestScore = child->lookaheadScore;
bestChild = child.get();
}
}
}
// If no child was visited, fall back to lookahead score
if (!bestChild && !children.empty()) {
for (const auto& child : children) {
if (child->isRedundant) continue;
const bool shouldReplace = isMaximizingPlayer ? (child->lookaheadScore > bestScore)
: (child->lookaheadScore < bestScore);
if (shouldReplace) {
bestScore = child->lookaheadScore;
bestChild = child.get();
}
}
}
return bestChild;
}
};
} // namespace mcts
} // namespace shardok
#endif // EAGLE0_ABSTRACT_MCTSNODE_HPP
@@ -1,60 +0,0 @@
//
// Core types for abstract MCTS implementation
//
#ifndef EAGLE0_MCTS_TYPES_HPP
#define EAGLE0_MCTS_TYPES_HPP
#include <stdexcept>
#include <string>
namespace shardok {
namespace mcts {
// Exception thrown when MCTS encounters an internal error that indicates a bug
class MCTSInternalError : public std::logic_error {
public:
explicit MCTSInternalError(const std::string& message) : std::logic_error(message) {}
};
// Abstract player identifier type
using MCTSPlayerId = int;
// Simulation policy for MCTS rollouts
enum class MCTSSimulationPolicy {
RANDOM, // Pure random selection
FILTERED_RANDOM, // Random from filtered actions
BEST_IMMEDIATE, // Choose best immediate score
WEIGHTED_BEST_IMMEDIATE, // Random weighted by score ranking
WEIGHTED_HEURISTIC // Random weighted by fast heuristics (no score evaluation)
};
// Backpropagation policy for MCTS tree updates
enum class MCTSBackpropagationPolicy {
AVERAGING, // Traditional MCTS averaging (for stochastic/single-player games)
MINIMAX // Minimax backup (for deterministic adversarial games)
};
// Configuration for MCTS algorithm
struct MCTSConfig {
double explorationConstant = 1.414; // UCB1 constant (sqrt(2) by default)
int maxSimulationDepth = 1000; // Maximum depth for rollout
int maxTreeDepth = 2000; // Maximum tree depth to prevent stack overflow
bool useMultithreading = true; // Enable parallel MCTS
int numThreads = 16; // Number of threads for parallel MCTS
MCTSSimulationPolicy simulationPolicy = MCTSSimulationPolicy::BEST_IMMEDIATE;
MCTSBackpropagationPolicy backpropagationPolicy = MCTSBackpropagationPolicy::AVERAGING;
int maxPlayerFlips = 0; // Maximum number of player changes for tree expansion
// (0 = expand through current player's turn only,
// 1 = expand through opponent's first response, etc.)
int maxSimulationFlips = 0; // Maximum player flips for leaf evaluation
// When evaluating a leaf at playerFlips < maxSimulationFlips,
// simulate forward to this phase for fair comparison
// (default 0 = evaluate leaves as-is, backward compatible)
std::string debugDumpPath = ""; // If non-empty, dump MCTS tree to this file path
};
} // namespace mcts
} // namespace shardok
#endif // EAGLE0_MCTS_TYPES_HPP
@@ -1,8 +0,0 @@
load("@rules_cc//cc:defs.bzl", "cc_library")
cc_library(
name = "tree_indent_util",
srcs = ["TreeIndentUtil.cpp"],
hdrs = ["TreeIndentUtil.hpp"],
visibility = ["//visibility:public"],
)
@@ -1,53 +0,0 @@
//
// Utility functions for processing tree indentation with UTF-8 box drawing characters
//
#include "TreeIndentUtil.hpp"
namespace mcts::util {
namespace {
// Box drawing characters for tree visualization
constexpr const char* kBranch = "\xE2\x94\x9C"; // ├
constexpr const char* kCorner = "\xE2\x94\x94"; // └
constexpr const char* kVertical = "\xE2\x94\x82"; // │
constexpr const char* kHorizontal = "\xE2\x94\x80"; // ─
} // namespace
std::string BuildTreeIndent(int indentLevel, bool isLastChild) {
std::string indent;
for (int i = 0; i < indentLevel; ++i) {
if (i == indentLevel - 1) {
indent += isLastChild ? kCorner : kBranch;
indent += kHorizontal;
indent += " ";
} else {
indent += " ";
}
}
return indent;
}
std::string ConvertBranchToContinuation(const std::string& indent) {
std::string result = indent;
const std::string replacement = std::string(kVertical) + " ";
// Replace ├ and └ with │
size_t pos = 0;
while ((pos = result.find(kBranch, pos)) != std::string::npos) {
result.replace(pos, 3, replacement); // UTF-8 chars are 3 bytes
pos += replacement.size();
}
pos = 0;
while ((pos = result.find(kCorner, pos)) != std::string::npos) {
result.replace(pos, 3, replacement);
pos += replacement.size();
}
return result;
}
} // namespace mcts::util
@@ -1,22 +0,0 @@
//
// Utility functions for processing tree indentation with UTF-8 box drawing characters
//
#ifndef EAGLE0_TREE_INDENT_UTIL_HPP
#define EAGLE0_TREE_INDENT_UTIL_HPP
#include <string>
namespace mcts::util {
// Builds tree indentation string for a node at a given depth
// Returns string like " ├─ " or " └─ " with proper spacing
std::string BuildTreeIndent(int indentLevel, bool isLastChild);
// Converts tree branch characters (├ and └) to continuation lines (│) for sub-content
// This preserves the tree structure when displaying additional info below a node
std::string ConvertBranchToContinuation(const std::string& indent);
} // namespace mcts::util
#endif // EAGLE0_TREE_INDENT_UTIL_HPP
@@ -36,7 +36,7 @@ auto CalculateMap(
.name = mapName,
.positionsRequiringCrossing = {}};
for (unsigned int i = 0; i < hexMap->attacker_starting_positions()->size(); i++) {
for (int i = 0; i < hexMap->attacker_starting_positions()->size(); i++) {
const auto* positionList = hexMap->attacker_starting_positions()->Get(i);
if (positionList->positions()->size() < 1) continue;
if (positionList->positions()->size() != 10) {
@@ -5,9 +5,7 @@
#ifndef EAGLE0_MAPINFOCALCULATOR_HPP
#define EAGLE0_MAPINFOCALCULATOR_HPP
#include <cstdint>
#include <map>
#include <memory>
#include <string>
#include "src/main/cpp/net/eagle0/shardok/library/action_point_distances/ActionPointDistancesCache.hpp"
@@ -3,7 +3,6 @@
//
#include <iostream>
#include <memory>
#include "MapInfoCalculator.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/settings/GameSettings.hpp"
@@ -53,7 +52,7 @@ auto main(const int argc, char** argv) -> int {
outputStream << " \"positions\": {";
bool firstPosition = true;
for (const auto& [position, count] : mapInfo.positionsRequiringCrossing) {
for (const auto& kv : mapInfo.positionsRequiringCrossing) {
if (firstPosition) {
outputStream << endl;
firstPosition = false;
@@ -61,7 +60,7 @@ auto main(const int argc, char** argv) -> int {
outputStream << "," << endl;
}
outputStream << " \"" << position << "\": " << count;
outputStream << " \"" << kv.first << "\": " << kv.second;
}
outputStream << endl << " }" << endl << " }";
}
@@ -4,11 +4,9 @@
#include "AIAttackGroups.hpp"
#include <cstdlib>
#include <iterator>
#include <ranges>
#include <unordered_map>
#include "src/main/cpp/net/eagle0/shardok/library/settings/GameSettings.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/util/HexMapUtils.hpp"
namespace shardok {
@@ -75,28 +73,30 @@ auto MinDistanceIncludingBraving(
auto EffectiveDistance(
const Unit* unit,
const HexMap* map,
const AttackLocations& attackLocations,
const MapId& mapId,
const APDCache& apdCache,
const BattalionTypeGetter& battalionTypeGetter,
ActionPoints braveWaterCost) -> DIST_T {
const AttackLocations& attackLocations,
const SettingsGetter& settings,
const int braveWaterCost) -> DIST_T {
return EffectiveDistance(
unit,
map,
attackLocations.LocationsWithEnemyInRange(unit),
mapId,
apdCache,
battalionTypeGetter,
attackLocations.LocationsWithEnemyInRange(unit),
settings,
braveWaterCost);
}
auto EffectiveDistance(
const Unit* unit,
const HexMap* map,
const CoordsSet& locations,
const MapId& mapId,
const APDCache& apdCache,
const BattalionTypeGetter& battalionTypeGetter,
ActionPoints braveWaterCost) -> DIST_T {
const auto mapId = ActionPointDistancesCache::GetMapId(map);
const auto& battType = battalionTypeGetter(unit->battalion().type());
const CoordsSet& locations,
const SettingsGetter& settings,
const int braveWaterCost) -> DIST_T {
const auto& battType = settings.GetBattalionType(unit->battalion().type());
const auto* notBravingApd = apdCache->GetRaw(map, mapId, battType, false);
const ActionPointDistances* bravingApd = nullptr;
if (battType->allowsBraveWater) {
@@ -129,12 +129,12 @@ auto GenerateTargetPriorities(
const vector<const Unit*>& remainingUnits,
const APDCache& apdCache,
const ALCache& alCache,
const BattalionTypeGetter& battalionTypeGetter,
ActionPoints braveWaterCost,
const MapId& mapId,
const SettingsGetter& settings,
const bool isLateGame) -> vector<TargetPriorityList> {
auto cc = map->column_count();
const auto mapId = ActionPointDistancesCache::GetMapId(map);
const auto braveWaterCost = settings.Backing().brave_water_action_point_cost();
vector<TargetPriorityList> allTargetsUnitsAndDistances{};
allTargetsUnitsAndDistances.reserve(remainingUnits.size());
@@ -158,7 +158,7 @@ auto GenerateTargetPriorities(
vector<TargetAndDistance> targetsWithDistance;
// Get APDs directly from cache (now with built-in thread-local optimization)
const auto& battType = battalionTypeGetter(unit->battalion().type());
const auto& battType = settings.GetBattalionType(unit->battalion().type());
const auto* notBravingApd = apdCache->GetRaw(map, mapId, battType, false);
const ActionPointDistances* bravingApd = nullptr;
if (battType->allowsBraveWater) {
@@ -221,15 +221,11 @@ auto GenerateTargetPriorities(
Power(unit);
}
tpl.priorityOrder.reserve(targetsWithDistance.size());
std::ranges::transform(
targetsWithDistance,
std::back_inserter(tpl.priorityOrder),
[](const TargetAndDistance& tad) {
return TargetAndAttackLocations{
.target = tad.target,
.attackLocations = tad.attackLocations};
});
tpl.priorityOrder = common::Map(targetsWithDistance, [](const TargetAndDistance& tad) {
return TargetAndAttackLocations{
.target = tad.target,
.attackLocations = tad.attackLocations};
});
}
return allTargetsUnitsAndDistances;
@@ -5,12 +5,12 @@
#ifndef EAGLE0_AIATTACKGROUPS_HPP
#define EAGLE0_AIATTACKGROUPS_HPP
#include <functional>
#include <vector>
#include "src/main/cpp/net/eagle0/shardok/ai/AIAttackLocations.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/action_point_distances/ActionPointDistancesCache.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/map/CoordsSet.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/settings/GameSettings.hpp"
#include "src/main/flatbuffer/net/eagle0/shardok/storage/game_state.hpp"
#include "src/main/flatbuffer/net/eagle0/shardok/storage/player_info.hpp"
#include "src/main/flatbuffer/net/eagle0/shardok/storage/unit.hpp"
@@ -22,8 +22,6 @@ using Unit = net::eagle0::shardok::storage::fb::Unit;
using net::eagle0::shardok::storage::fb::PlayerInfo;
using std::vector;
using BattalionTypeGetter = std::function<BattalionTypeSPtr(BattalionTypeId)>;
struct TargetAndAttackLocations {
Coords target;
CoordsSet attackLocations;
@@ -43,18 +41,20 @@ struct TargetPriorityList {
auto EffectiveDistance(
const Unit* unit,
const HexMap* map,
const AttackLocations& attackLocations,
const MapId& mapId,
const APDCache& apdCache,
const BattalionTypeGetter& battalionTypeGetter,
ActionPoints braveWaterCost) -> DIST_T;
const AttackLocations& attackLocations,
const SettingsGetter& settings,
int braveWaterCost) -> DIST_T;
auto EffectiveDistance(
const Unit* unit,
const HexMap* map,
const CoordsSet& locations,
const MapId& mapId,
const APDCache& apdCache,
const BattalionTypeGetter& battalionTypeGetter,
ActionPoints braveWaterCost) -> DIST_T;
const CoordsSet& locations,
const SettingsGetter& settings,
int braveWaterCost) -> DIST_T;
auto EffectiveDistance(
const Unit* unit,
@@ -71,8 +71,8 @@ auto GenerateTargetPriorities(
const vector<const Unit*>& remainingUnits,
const APDCache& apdCache,
const ALCache& alCache,
const BattalionTypeGetter& battalionTypeGetter,
ActionPoints braveWaterCost,
const MapId& mapId,
const SettingsGetter& settings,
bool isLateGame = false) -> vector<TargetPriorityList>;
} // namespace shardok
@@ -4,8 +4,6 @@
#include "AIAttackerStrategySelector.hpp"
#include "AIAttackGroups.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AIFleeDecisionCalculator.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AIScoreUtilities.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/util/HexMapUtils.hpp"
@@ -13,23 +11,21 @@ namespace shardok {
using Unit = net::eagle0::shardok::storage::fb::Unit;
// Combat success threshold below which we should consider fleeing
// This replaces the simple troop ratio check with sophisticated probability estimation
constexpr double FLEE_CONSIDERATION_THRESHOLD = 0.25;
constexpr double MAXIMUM_RATIO_FOR_ATTACKER_TO_FLEE = 0.50;
auto AIAttackerStrategySelector::BestAttackerStrategy(
const PlayerId attackerPid,
const GameStateW& gameState,
const net::eagle0::shardok::storage::fb::GameState* gameState,
const CoordsSet& criticalTileCoords,
int maxRounds,
const APDCache& apdCache,
const ALCache& alCache,
const BattalionTypeGetter& battalionTypeGetter,
ActionPoints braveWaterCost,
const SettingsGetter& settings,
const AIWaterCrossingCommandChooser& waterCrossingCommandChooser,
const CommandListSPtr& /*availableCommands*/) -> AIStrategy {
const vector<CommandProto>& availableCommands) -> AIStrategy {
uint32_t attackerUnitCount = 0;
int defenderOccupiedCriticalTileCount = 0;
int attackerTroops = 0;
int defenderTroops = 0;
bool canFlee = false;
vector<const Unit*> attackerUnits{};
@@ -44,6 +40,8 @@ auto AIAttackerStrategySelector::BestAttackerStrategy(
if (pi != nullptr) {
if (pi->is_defender()) {
if (unit->location().row() >= 0) {
defenderTroops += unit->battalion().size();
if (criticalTileCoords.Contains(unit->location())) {
++defenderOccupiedCriticalTileCount;
}
@@ -52,6 +50,7 @@ auto AIAttackerStrategySelector::BestAttackerStrategy(
}
} else if (unit->player_id() == attackerPid) {
++attackerUnitCount;
attackerTroops += unit->battalion().size();
if (unit->can_flee()) canFlee = true;
attackerUnits.push_back(unit);
} else {
@@ -61,19 +60,11 @@ auto AIAttackerStrategySelector::BestAttackerStrategy(
}
AIStrategy chosenStrategy;
// Use sophisticated combat success estimation instead of simple troop ratio
if (canFlee && AIFleeDecisionCalculator::ShouldConsiderFleeing(
attackerPid,
gameState,
maxRounds,
FLEE_CONSIDERATION_THRESHOLD)) {
if (canFlee && attackerTroops < MAXIMUM_RATIO_FOR_ATTACKER_TO_FLEE * defenderTroops) {
chosenStrategy = FleeStrategy;
} else if (const CoordsSet startCrossingLocations =
waterCrossingCommandChooser.StartCrossingFrom(
battalionTypeGetter,
gameState,
criticalTileCoords);
waterCrossingCommandChooser
.StartCrossingFrom(settings, gameState, criticalTileCoords);
!startCrossingLocations.empty()) {
chosenStrategy = CrossRiversStrategy(startCrossingLocations);
} else if (attackerUnitCount < criticalTileCoords.size()) {
@@ -88,8 +79,8 @@ auto AIAttackerStrategySelector::BestAttackerStrategy(
attackerUnits,
apdCache,
alCache,
battalionTypeGetter,
braveWaterCost));
ActionPointDistancesCache::GetMapId(gameState->hex_map()),
settings));
}
// If any critical tile is occupied by the defender, attack the castles.
// Otherwise, try to hold the castles.
@@ -105,8 +96,8 @@ auto AIAttackerStrategySelector::BestAttackerStrategy(
attackerUnits,
apdCache,
alCache,
battalionTypeGetter,
braveWaterCost));
ActionPointDistancesCache::GetMapId(gameState->hex_map()),
settings));
} else {
chosenStrategy = HoldCastlesStrategy;
}
@@ -6,29 +6,26 @@
#define EAGLE0_AIATTACKERSTRATEGYSELECTOR_HPP
#include "src/main/cpp/net/eagle0/shardok/ai/AIAttackLocations.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AICommonTypes.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AIStrategy.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AIWaterCrossingCommandChooser.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/GameStateW.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/ShardokCommand.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/action_point_distances/ActionPointDistancesCache.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/map/CoordsSet.hpp"
#include "src/main/flatbuffer/net/eagle0/shardok/storage/game_state.hpp"
namespace shardok {
using GameState = net::eagle0::shardok::storage::fb::GameState;
class AIAttackerStrategySelector {
public:
static auto BestAttackerStrategy(
PlayerId attackerPid,
const GameStateW& gameState,
const GameState* gameState,
const CoordsSet& criticalTileCoords,
int maxRounds,
const APDCache& apdCache,
const ALCache& alCache,
const BattalionTypeGetter& battalionTypeGetter,
ActionPoints braveWaterCost,
const SettingsGetter& settings,
const AIWaterCrossingCommandChooser& waterCrossingCommandChooser,
const CommandListSPtr& availableCommands) -> AIStrategy;
const vector<CommandProto>& availableCommands) -> AIStrategy;
};
} // namespace shardok
@@ -1,560 +0,0 @@
//
// Command evaluator for AI lookahead search.
// Extracted from AIScoreCalculator to separate concerns.
//
#include "AICommandEvaluator.hpp"
#include <chrono>
#include <cmath>
#include <future>
#include <limits>
#include "AICommandFilter.hpp"
#include "TranspositionTable.hpp"
#include "src/main/cpp/net/eagle0/common/SequenceRandomGenerator.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/score/AIScoreCalculator.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/ShardokEngine.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/util/HexCubeUtils.hpp"
namespace shardok {
// No need to forward declare internal functions - use the public interface instead
// Helper constants and static variables
static const std::vector<double> _averageSequence = {0.5};
static const auto _averageGenerator = std::make_shared<SequenceRandomGenerator>(_averageSequence);
#define MULTITHREAD true
#define LOGGING_ 0
// Helper function to determine if a command type is deterministic
static auto IsDeterministic(const CommandType type) -> bool {
switch (type) {
case net::eagle0::shardok::common::MOVE_COMMAND:
case net::eagle0::shardok::common::CONTROL_COMMAND:
case net::eagle0::shardok::common::METEOR_START_COMMAND:
case net::eagle0::shardok::common::METEOR_TARGET_COMMAND:
case net::eagle0::shardok::common::METEOR_CANCEL_COMMAND:
case net::eagle0::shardok::common::END_TURN_COMMAND:
case net::eagle0::shardok::common::PLACE_UNIT_COMMAND:
case net::eagle0::shardok::common::PLACE_HIDDEN_UNIT_COMMAND:
case net::eagle0::shardok::common::UNIT_STOP_COMMAND:
case net::eagle0::shardok::common::UNIT_REST_COMMAND:
case net::eagle0::shardok::common::FLEE_COMMAND:
case net::eagle0::shardok::common::REINFORCE_COMMAND:
case net::eagle0::shardok::common::RETREAT_COMMAND:
case net::eagle0::shardok::common::END_PLAYER_SETUP_COMMAND:
case net::eagle0::shardok::common::HIDE_COMMAND:
case net::eagle0::shardok::common::FORTIFY_COMMAND:
case net::eagle0::shardok::common::BECOME_OUTLAW_COMMAND:
case net::eagle0::shardok::common::HOLY_WAVE_COMMAND:
case net::eagle0::shardok::common::REPAIR_COMMAND: return true;
default: return false;
}
}
// Helper function to sort commands by score
static auto CommandSorter(
const AICommandEvaluator::IndexAndScore& l,
const AICommandEvaluator::IndexAndScore& r) -> bool {
if (l.lookaheadScore < r.lookaheadScore) return true;
if (l.lookaheadScore > r.lookaheadScore) return false;
// At this point the scores are tied
if (l.immediateScore < r.immediateScore) return true;
if (l.immediateScore > r.immediateScore) return false;
return false;
}
AICommandEvaluator::AICommandEvaluator(
const AIScoreCalculator& scorer,
const APDCache& apdCache,
BattalionTypeGetter battalionTypeGetter)
: scorer_(scorer),
apdCache_(apdCache),
battalionTypeGetter_(std::move(battalionTypeGetter)) {} // Move the function object
auto AICommandEvaluator::PerformLookahead(
const PlayerId pid,
const bool isDefender,
const int remainingLookahead,
const int maxRepeatCount,
const std::shared_ptr<ShardokEngine>& innerEngine,
const ScoreValue currentUtility,
const AIStrategy& attackerStrategy,
const CoordsSet& allCastleCoords,
std::chrono::steady_clock::time_point deadline) const -> std::future<ScoreValue> {
// Check transposition table before expensive computation
auto cachedScore =
g_transpositionTable.probe(innerEngine->GetCurrentGameState(), remainingLookahead, pid);
if (cachedScore.has_value()) {
// Return cached result immediately
std::promise<ScoreValue> p;
p.set_value(*cachedScore);
return p.get_future();
}
const auto nextUtility = currentUtility;
// Check if we've reached the depth limit before making recursive calls
if (remainingLookahead <= 0) {
// Store the current utility in the transposition table and return it
// Note: Store with depth 1 since depth 0 indicates an empty entry in the transposition
// table
g_transpositionTable.store(innerEngine->GetCurrentGameState(), 1, pid, nextUtility);
std::promise<ScoreValue> p;
p.set_value(nextUtility);
return p.get_future();
}
if (const CommandListSPtr nextCommands = innerEngine->GetAvailableCommandsForAIPlayer(pid);
nextCommands && !nextCommands->empty()) {
// Get the future from FindBestCommand without calling .get()
auto bestCommandFuture = FindBestCommand(
pid,
isDefender,
remainingLookahead - 1,
maxRepeatCount,
*innerEngine,
attackerStrategy,
nextUtility,
allCastleCoords,
deadline);
// Return a future that chains the best command evaluation
return std::async(
std::launch::deferred,
[bestCommandFuture = std::move(bestCommandFuture),
innerEngine,
pid,
nextUtility,
remainingLookahead]() mutable -> ScoreValue {
const auto [index, type, lookaheadScore, immediateScore] =
bestCommandFuture.get();
ScoreValue resultScore;
if (auto& nextCommand =
innerEngine->GetAvailableCommandsForAIPlayer(pid)->at(index);
nextCommand->GetCommandType() !=
net::eagle0::shardok::common::END_TURN_COMMAND) {
resultScore = immediateScore;
} else {
resultScore = nextUtility;
}
// Store in transposition table before returning
g_transpositionTable.store(
innerEngine->GetCurrentGameState(),
remainingLookahead,
pid,
resultScore);
return resultScore;
});
}
// No commands available, store and return the current utility as a future
g_transpositionTable
.store(innerEngine->GetCurrentGameState(), remainingLookahead, pid, nextUtility);
std::promise<ScoreValue> p;
p.set_value(nextUtility);
return p.get_future();
}
auto AICommandEvaluator::EvaluateWithRandomness(
const PlayerId pid,
const bool isDefender,
const uint32_t commandIndex,
const int remainingLookahead,
const int maxRepeatCount,
const std::shared_ptr<RandomGenerator>& randomGenerator,
const ShardokEngine& guessedEngine,
const AIStrategy& attackerStrategy,
const CoordsSet& allCastleCoords,
std::chrono::steady_clock::time_point deadline) const -> ImmediateAndLookaheadScore {
ImmediateAndLookaheadScore returnValue{};
// Check if we've exceeded the deadline
if (std::chrono::steady_clock::now() > deadline) {
// Return with a default score and an empty future that resolves immediately
std::promise<ScoreValue> p;
p.set_value(0.0); // Default timeout score
returnValue.immediateScore = 0.0;
returnValue.lookaheadScore = p.get_future();
return returnValue;
}
auto innerEngine = std::make_shared<ShardokEngine>(guessedEngine, false);
innerEngine->PostCommand(pid, commandIndex, randomGenerator);
auto innerUtility = scorer_.GuessedStateScore(
isDefender,
innerEngine->GetCurrentGameState(),
attackerStrategy,
allCastleCoords);
returnValue.immediateScore = innerUtility;
if (remainingLookahead <= 0) {
std::promise<ScoreValue> p;
returnValue.lookaheadScore = p.get_future();
p.set_value(innerUtility);
} else {
auto lookaheadLambda = [this,
pid,
isDefender,
remainingLookahead,
maxRepeatCount,
innerEngine,
attackerStrategy,
innerUtility,
&allCastleCoords,
deadline]() -> ScoreValue {
auto lookaheadFuture = PerformLookahead(
pid,
isDefender,
remainingLookahead,
maxRepeatCount,
innerEngine,
innerUtility,
attackerStrategy,
allCastleCoords,
deadline);
return lookaheadFuture.get();
};
#if MULTITHREAD
auto launchPolicy = remainingLookahead == 1 ? std::launch::async : std::launch::deferred;
returnValue.lookaheadScore = std::async(launchPolicy, lookaheadLambda);
#else
std::promise<ScoreValue> p;
returnValue.lookaheadScore = p.get_future();
auto lambdaResult = lookaheadLambda();
p.set_value(lambdaResult);
#endif
}
return returnValue;
}
auto AICommandEvaluator::FindBestCommand(
const PlayerId pid,
const bool isDefender,
const int remainingLookahead,
const int maxRepeatCount,
const ShardokEngine& guessedEngine,
const AIStrategy& attackerStrategy,
const ScoreValue currentUtility,
const CoordsSet& allCastleCoords,
std::chrono::steady_clock::time_point deadline) const -> std::future<IndexAndScore> {
const CommandListSPtr guessedDescriptors = guessedEngine.GetAvailableCommandsForAIPlayer(pid);
// Filter out obviously bad commands to reduce search space
const std::vector<size_t> filteredIndices = AICommandFilter::FilterCommands(
guessedDescriptors,
pid,
isDefender,
guessedEngine.GetCurrentGameState(),
apdCache_,
battalionTypeGetter_);
const auto& gameState = guessedEngine.GetCurrentGameState();
// Calculate minimum hex distance to enemies for this player
double minDistToEnemies = std::numeric_limits<double>::max();
const auto* units = gameState->units();
for (size_t i = 0; i < units->size(); ++i) {
if (const auto* playerUnit = units->Get(static_cast<unsigned int>(i));
playerUnit->player_id() == pid) {
const auto& playerCoords = playerUnit->location();
for (size_t j = 0; j < units->size(); ++j) {
if (const auto* enemyUnit = units->Get(static_cast<unsigned int>(j));
enemyUnit->player_id() != pid) {
const auto& enemyCoords = enemyUnit->location();
// Proper hex distance calculation using cube coordinates
const Cube playerCube = OffsetToCube(playerCoords);
const Cube enemyCube = OffsetToCube(enemyCoords);
const int hexDistance = CubeDistance(playerCube, enemyCube);
minDistToEnemies = std::min(minDistToEnemies, static_cast<double>(hexDistance));
}
}
}
}
if (minDistToEnemies == std::numeric_limits<double>::max()) {
minDistToEnemies = 0.0; // No enemies found
}
#if LOGGING_
// Log command count and distance metrics for performance analysis
const auto allCommandCount = guessedDescriptors->size();
const auto filteredCommandCount = filteredIndices.size();
const int currentRound = gameState->current_round();
printf("AI_COMMAND_COUNT: Round %d, Player %d, Defender %d, MinDist %.1f, Commands %zu -> %zu "
"(%.1f%% filtered)\n",
currentRound,
static_cast<int>(pid),
isDefender ? 1 : 0,
minDistToEnemies,
allCommandCount,
filteredCommandCount,
100.0 * (allCommandCount - filteredCommandCount) / allCommandCount);
#endif
const auto commandCount = filteredIndices.size();
// Structure to hold all command evaluation data
struct CommandEvaluation {
size_t index;
CommandType type;
ScoreValue immediateScore;
std::vector<std::future<ScoreValue>> lookaheadFutures;
};
std::vector<CommandEvaluation> commandEvaluations(commandCount);
for (uint32_t index = 0; index < commandCount; index++) {
const auto originalIndex = filteredIndices[index];
const auto& guessedDescriptor = guessedDescriptors->at(originalIndex);
const auto guessedCommandType = guessedDescriptor->GetCommandType();
commandEvaluations[index].index = originalIndex;
commandEvaluations[index].type = guessedCommandType;
if (guessedCommandType == net::eagle0::shardok::common::END_TURN_COMMAND) {
std::promise<ScoreValue> p;
commandEvaluations[index].lookaheadFutures.push_back(p.get_future());
p.set_value(currentUtility);
commandEvaluations[index].immediateScore = currentUtility;
} else if (IsDeterministic(guessedCommandType)) {
auto [immediateScore, lookaheadScore] = EvaluateWithRandomness(
pid,
isDefender,
originalIndex,
remainingLookahead,
maxRepeatCount,
_averageGenerator,
guessedEngine,
attackerStrategy,
allCastleCoords,
deadline);
commandEvaluations[index].immediateScore = immediateScore;
commandEvaluations[index].lookaheadFutures.push_back(std::move(lookaheadScore));
} else if (guessedDescriptor->HasOdds()) {
const auto successChancePercentile = guessedDescriptor->GetOddsPercentile();
const double successChance = static_cast<double>(successChancePercentile) / 100.0;
// Success attempt uses 1.0 - (successChance / 2) as the roll
auto [successImmediateScore, successLookaheadScore] = EvaluateWithRandomness(
pid,
isDefender,
originalIndex,
remainingLookahead,
maxRepeatCount,
std::make_shared<SequenceRandomGenerator>(
std::vector{1.0 - successChance / 2.0}),
guessedEngine,
attackerStrategy,
allCastleCoords,
deadline);
// Failure attempt uses the average of (1 - successChance) and 0 as the roll
auto [failureImmediateScore, failureLookaheadScore] = EvaluateWithRandomness(
pid,
isDefender,
originalIndex,
remainingLookahead,
maxRepeatCount,
std::make_shared<SequenceRandomGenerator>(
std::vector{(1.0 - successChance) / 2.0}),
guessedEngine,
attackerStrategy,
allCastleCoords,
deadline);
commandEvaluations[index].immediateScore =
std::lerp(failureImmediateScore, successImmediateScore, successChance);
auto successSF = successLookaheadScore.share();
auto failureSF = failureLookaheadScore.share();
commandEvaluations[index].lookaheadFutures.push_back(std::async(
std::launch::deferred,
[successSF, failureSF, successChance]() -> double {
return std::lerp(failureSF.get(), successSF.get(), successChance);
}));
} else {
ScoreValue sum = 0.0;
for (int repeatIteration = 0; repeatIteration < maxRepeatCount; repeatIteration++) {
// In each iteration, use a double from [0, 1] as the random roll
auto sequence = std::vector{
static_cast<double>(repeatIteration) /
static_cast<double>(maxRepeatCount - 1)};
auto [immediateScore, lookaheadScore] = EvaluateWithRandomness(
pid,
isDefender,
originalIndex,
remainingLookahead,
maxRepeatCount,
std::make_shared<SequenceRandomGenerator>(sequence),
guessedEngine,
attackerStrategy,
allCastleCoords,
deadline);
sum += immediateScore;
commandEvaluations[index].lookaheadFutures.push_back(std::move(lookaheadScore));
}
commandEvaluations[index].immediateScore = sum / maxRepeatCount;
}
}
// Return a future that will wait for all evaluations and find the best one
return std::async(
std::launch::deferred,
[evals = std::move(commandEvaluations)]() mutable -> IndexAndScore {
std::vector<IndexAndScore> allResults;
allResults.reserve(evals.size());
// Wait for all futures and compute final scores
for (auto& eval : evals) {
ScoreValue totalLookaheadScore = 0.0;
for (auto& future : eval.lookaheadFutures) {
totalLookaheadScore += future.get();
}
ScoreValue avgLookaheadScore =
eval.lookaheadFutures.empty()
? eval.immediateScore
: totalLookaheadScore / eval.lookaheadFutures.size();
allResults.push_back(IndexAndScore{
.index = eval.index,
.type = eval.type,
.lookaheadScore = avgLookaheadScore,
.immediateScore = eval.immediateScore});
}
// Find the best command using the existing sorter
auto bestIt = std::ranges::max_element(allResults, CommandSorter);
return *bestIt;
});
}
auto AICommandEvaluator::EvaluateCommand(
const PlayerId pid,
const bool isDefender,
const int remainingLookahead,
const int maxRepeatCount,
const ShardokEngine& guessedEngine,
const AIStrategy& attackerStrategy,
const ScoreValue currentUtility,
const CoordsSet& allCastleCoords,
const size_t commandIndex,
std::chrono::steady_clock::time_point deadline) const -> std::future<ScoreValue> {
const CommandListSPtr guessedDescriptors = guessedEngine.GetAvailableCommandsForAIPlayer(pid);
if (commandIndex >= guessedDescriptors->size()) {
std::promise<ScoreValue> p;
p.set_value(currentUtility);
return p.get_future();
}
const auto& guessedDescriptor = guessedDescriptors->at(commandIndex);
if (const auto guessedCommandType = guessedDescriptor->GetCommandType();
guessedCommandType == net::eagle0::shardok::common::END_TURN_COMMAND) {
std::promise<ScoreValue> p;
p.set_value(currentUtility);
return p.get_future();
} else if (IsDeterministic(guessedCommandType)) {
auto [immediateScore, lookaheadScore] = EvaluateWithRandomness(
pid,
isDefender,
commandIndex,
remainingLookahead,
maxRepeatCount,
_averageGenerator,
guessedEngine,
attackerStrategy,
allCastleCoords,
deadline);
return std::move(lookaheadScore);
} else if (guessedDescriptor->HasOdds()) {
const auto successChancePercentile = guessedDescriptor->GetOddsPercentile();
const double successChance = static_cast<double>(successChancePercentile) / 100.0;
// Success attempt
auto [successImmediateScore, successLookaheadScore] = EvaluateWithRandomness(
pid,
isDefender,
commandIndex,
remainingLookahead,
maxRepeatCount,
std::make_shared<SequenceRandomGenerator>(std::vector{1.0 - successChance / 2.0}),
guessedEngine,
attackerStrategy,
allCastleCoords,
deadline);
// Failure attempt
auto [failureImmediateScore, failureLookaheadScore] = EvaluateWithRandomness(
pid,
isDefender,
commandIndex,
remainingLookahead,
maxRepeatCount,
std::make_shared<SequenceRandomGenerator>(std::vector{(1.0 - successChance) / 2.0}),
guessedEngine,
attackerStrategy,
allCastleCoords,
deadline);
// Return weighted average of success and failure
auto successSF = successLookaheadScore.share();
auto failureSF = failureLookaheadScore.share();
return std::async(std::launch::deferred, [successSF, failureSF, successChance]() -> double {
return std::lerp(failureSF.get(), successSF.get(), successChance);
});
} else {
// For non-deterministic commands without odds, use multiple attempts
std::vector<std::future<ScoreValue>> lookaheadFutures;
lookaheadFutures.reserve(maxRepeatCount);
for (int repeatIteration = 0; repeatIteration < maxRepeatCount; repeatIteration++) {
auto sequence = std::vector{
static_cast<double>(repeatIteration) / static_cast<double>(maxRepeatCount - 1)};
auto [immediateScore, lookaheadScore] = EvaluateWithRandomness(
pid,
isDefender,
commandIndex,
remainingLookahead,
maxRepeatCount,
std::make_shared<SequenceRandomGenerator>(sequence),
guessedEngine,
attackerStrategy,
allCastleCoords,
deadline);
lookaheadFutures.push_back(std::move(lookaheadScore));
}
// Return a future that computes the average when needed
return std::async(
std::launch::deferred,
[lookaheadFutures = std::move(lookaheadFutures),
maxRepeatCount]() mutable -> double {
ScoreValue total = 0.0;
for (auto& future : lookaheadFutures) { total += future.get(); }
return total / maxRepeatCount;
});
}
}
} // namespace shardok
@@ -1,110 +0,0 @@
//
// Command evaluator for AI lookahead search.
// Separated from AIScoreCalculator to isolate pure state scoring from lookahead logic.
//
#ifndef EAGLE0_AICOMMANDEVALUATOR_HPP
#define EAGLE0_AICOMMANDEVALUATOR_HPP
#include <chrono>
#include <future>
#include "src/main/cpp/net/eagle0/shardok/ai/AIStrategy.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/GameStateW.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/ShardokCTypes.h"
#include "src/main/cpp/net/eagle0/shardok/library/action_point_distances/ActionPointDistancesCache.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/map/CoordsSet.hpp"
#include "src/main/protobuf/net/eagle0/shardok/common/command_type.pb.h"
namespace shardok {
// Forward declarations
class AIScoreCalculator;
class ShardokEngine;
using ScoreValue = double;
using CommandType = net::eagle0::shardok::common::CommandType;
using BattalionTypeGetter = std::function<BattalionTypeSPtr(BattalionTypeId)>;
/// Evaluates commands with lookahead using minimax-style search.
/// Uses AIScoreCalculator for pure state evaluation, adds recursive lookahead logic.
class AICommandEvaluator {
public:
/// Construct evaluator with a scorer for state evaluation and dependencies for command
/// filtering
AICommandEvaluator(
const AIScoreCalculator& scorer,
const APDCache& apdCache,
BattalionTypeGetter battalionTypeGetter); // Pass by value
/// Evaluates the score for a particular command index with lookahead.
[[nodiscard]] auto EvaluateCommand(
PlayerId pid,
bool isDefender,
int remainingLookahead,
int maxRepeatCount,
const ShardokEngine& guessedEngine,
const AIStrategy& attackerStrategy,
ScoreValue currentUtility,
const CoordsSet& allCastleCoords,
size_t commandIndex,
std::chrono::steady_clock::time_point deadline) const -> std::future<ScoreValue>;
/// Find the best command among all available commands at the given depth.
struct IndexAndScore {
size_t index;
CommandType type;
ScoreValue lookaheadScore;
ScoreValue immediateScore;
};
[[nodiscard]] auto FindBestCommand(
PlayerId pid,
bool isDefender,
int remainingLookahead,
int maxRepeatCount,
const ShardokEngine& guessedEngine,
const AIStrategy& attackerStrategy,
ScoreValue currentUtility,
const CoordsSet& allCastleCoords,
std::chrono::steady_clock::time_point deadline) const -> std::future<IndexAndScore>;
private:
const AIScoreCalculator& scorer_;
const APDCache& apdCache_;
BattalionTypeGetter battalionTypeGetter_; // Store by value
struct ImmediateAndLookaheadScore {
ScoreValue immediateScore;
std::future<ScoreValue> lookaheadScore;
};
/// Recursive lookahead calculator
[[nodiscard]] auto PerformLookahead(
PlayerId pid,
bool isDefender,
int remainingLookahead,
int maxRepeatCount,
const std::shared_ptr<ShardokEngine>& innerEngine,
ScoreValue currentUtility,
const AIStrategy& attackerStrategy,
const CoordsSet& allCastleCoords,
std::chrono::steady_clock::time_point deadline) const -> std::future<ScoreValue>;
/// Evaluate single command execution with randomness handling
[[nodiscard]] auto EvaluateWithRandomness(
PlayerId pid,
bool isDefender,
uint32_t commandIndex,
int remainingLookahead,
int maxRepeatCount,
const std::shared_ptr<class RandomGenerator>& randomGenerator,
const ShardokEngine& guessedEngine,
const AIStrategy& attackerStrategy,
const CoordsSet& allCastleCoords,
std::chrono::steady_clock::time_point deadline) const -> ImmediateAndLookaheadScore;
};
} // namespace shardok
#endif // EAGLE0_AICOMMANDEVALUATOR_HPP
@@ -7,7 +7,6 @@
#include <algorithm>
#include "src/main/cpp/net/eagle0/shardok/library/BattalionType.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/ShardokException.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/util/HexCubeUtils.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/util/HexMapUtils.hpp"
#include "src/main/protobuf/net/eagle0/shardok/common/command_type.pb.h"
@@ -21,8 +20,8 @@ CoordsSet AICommandFilter::BuildEnemyLocations(const GameStateW& gameState, Play
CoordsSet enemyLocations(gameState->hex_map());
const auto* units = gameState->units();
for (size_t i = 0; i < units->size(); ++i) {
const auto* unit = units->Get(static_cast<unsigned int>(i));
for (int i = 0; i < units->size(); ++i) {
const auto* unit = units->Get(i);
if (unit->status() == net::eagle0::shardok::storage::fb::UnitStatus_NORMAL_UNIT &&
unit->player_id() != pid && !unit->hidden() && unit->location().column() != -1) {
enemyLocations.Add(unit->location());
@@ -37,8 +36,8 @@ std::vector<size_t> AICommandFilter::FilterCommands(
PlayerId pid,
bool isDefender,
const GameStateW& gameState,
const APDCache& apdCache,
const BattalionTypeGetter& battalionTypeGetter) {
const SettingsGetter& settings,
const APDCache& apdCache) {
std::vector<size_t> filteredIndices;
filteredIndices.reserve(commands->size());
@@ -67,8 +66,8 @@ std::vector<size_t> AICommandFilter::FilterCommands(
pid,
isDefender,
gameState,
settings,
apdCache,
battalionTypeGetter,
enemyLocations,
castleLocations,
minDistToEnemies)) {
@@ -81,22 +80,16 @@ std::vector<size_t> AICommandFilter::FilterCommands(
pid,
isDefender,
gameState,
settings,
apdCache,
battalionTypeGetter,
enemyLocations,
minDistToEnemies)) {
shouldFilter = true;
}
// Check strategic blunders
if (!shouldFilter && IsStrategicBlunder(
*cmd,
pid,
isDefender,
gameState,
apdCache,
battalionTypeGetter,
minDistToEnemies)) {
if (!shouldFilter &&
IsStrategicBlunder(*cmd, pid, isDefender, gameState, settings, minDistToEnemies)) {
shouldFilter = true;
}
@@ -111,8 +104,8 @@ bool AICommandFilter::IsWastefulAction(
PlayerId pid,
bool isDefender,
const GameStateW& gameState,
const SettingsGetter& settings,
const APDCache& apdCache,
const BattalionTypeGetter& battalionTypeGetter,
const CoordsSet& enemyLocations,
const CoordsSet& castleLocations,
double minDistToEnemies) {
@@ -144,16 +137,15 @@ bool AICommandFilter::IsWastefulAction(
if (!isDefender) {
// Attackers: Only allow fire if the target location is on or adjacent to an enemy
const int targetRow = cmd.GetTargetRow();
const int targetCol = cmd.GetTargetColumn();
if (targetRow < 0 || targetCol < 0) {
throw ShardokInternalErrorException(
"START_FIRE_COMMAND missing required target information");
const auto cmdProto = cmd.GetCommandProto();
if (!cmdProto.has_target()) {
return true; // Can't analyze without target info
}
const Coords fireLocation(
static_cast<int8_t>(targetRow),
static_cast<int8_t>(targetCol));
const auto& targetCoords = cmdProto.target();
const Coords fireLocation{
static_cast<int8_t>(targetCoords.row()),
static_cast<int8_t>(targetCoords.column())};
// Check if any enemy is on the fire location or adjacent to it
bool enemyNearFireLocation = false;
@@ -188,12 +180,13 @@ bool AICommandFilter::IsWastefulAction(
if (!isDefender) {
// Attackers: Only allow fortify if within 3 hexes of enemies or castles
const int unitId = cmd.GetActorUnitId();
if (unitId < 0) {
throw ShardokInternalErrorException(
"FORTIFY_COMMAND missing required actor information");
const auto cmdProto = cmd.GetCommandProto();
if (!cmdProto.has_actor()) {
return true; // Can't analyze without actor info
}
const auto unitId = cmdProto.actor().value();
// Get the acting unit directly by ID
const Unit* actingUnit = gameState->units()->Get(unitId);
// verify the unit is still active
@@ -250,18 +243,16 @@ bool AICommandFilter::IsWastefulAction(
// These actions can fail, so we need high confidence of benefit (8+ action points
// saved)
const int unitId = cmd.GetActorUnitId();
const int targetRow = cmd.GetTargetRow();
const int targetCol = cmd.GetTargetColumn();
if (unitId < 0 || targetRow < 0 || targetCol < 0) {
throw ShardokInternalErrorException(
"BUILD_BRIDGE/FREEZE_WATER_COMMAND missing required actor or target "
"information");
const auto cmdProto = cmd.GetCommandProto();
if (!cmdProto.has_actor() || !cmdProto.has_target()) {
return true; // Can't analyze without full command info
}
const Coords waterLocation(
static_cast<int8_t>(targetRow),
static_cast<int8_t>(targetCol));
const auto unitId = cmdProto.actor().value();
const auto& targetCoords = cmdProto.target();
const Coords waterLocation{
static_cast<int8_t>(targetCoords.row()),
static_cast<int8_t>(targetCoords.column())};
// Get the acting unit directly by ID
const Unit* actingUnit = gameState->units()->Get(unitId);
@@ -278,7 +269,7 @@ bool AICommandFilter::IsWastefulAction(
}
// Get action point distances for this unit's battalion type
const auto& battType = battalionTypeGetter(actingUnit->battalion().type());
const auto& battType = settings.GetBattalionType(actingUnit->battalion().type());
const auto* apd = apdCache->GetRaw(
gameState->hex_map(),
ActionPointDistancesCache::GetMapId(gameState->hex_map()),
@@ -356,16 +347,15 @@ bool AICommandFilter::IsWastefulAction(
case CommandType::REPAIR_COMMAND: {
// Repair filtering - filter repairs with high integrity targets
// Note: RepairCommandFactory already filters enemy-occupied targets
const int targetRow = cmd.GetTargetRow();
const int targetCol = cmd.GetTargetColumn();
if (targetRow < 0 || targetCol < 0) {
throw ShardokInternalErrorException(
"REPAIR_COMMAND missing required target information");
const auto cmdProto = cmd.GetCommandProto();
if (!cmdProto.has_target()) {
return true; // Can't analyze without target info
}
const Coords repairLocation(
static_cast<int8_t>(targetRow),
static_cast<int8_t>(targetCol));
const auto& targetCoords = cmdProto.target();
const Coords repairLocation{
static_cast<int8_t>(targetCoords.row()),
static_cast<int8_t>(targetCoords.column())};
// Check terrain modifiers at target location
const auto* terrain = GetTerrain(gameState->hex_map(), repairLocation);
@@ -388,16 +378,15 @@ bool AICommandFilter::IsWastefulAction(
case CommandType::EXTINGUISH_FIRE_COMMAND: {
// Extinguish fire filtering - don't extinguish fires on enemy-occupied tiles
const int targetRow = cmd.GetTargetRow();
const int targetCol = cmd.GetTargetColumn();
if (targetRow < 0 || targetCol < 0) {
throw ShardokInternalErrorException(
"EXTINGUISH_FIRE_COMMAND missing required target information");
const auto cmdProto = cmd.GetCommandProto();
if (!cmdProto.has_target()) {
return true; // Can't analyze without target info
}
const Coords fireLocation(
static_cast<int8_t>(targetRow),
static_cast<int8_t>(targetCol));
const auto& targetCoords = cmdProto.target();
const Coords fireLocation{
static_cast<int8_t>(targetCoords.row()),
static_cast<int8_t>(targetCoords.column())};
// Check if any enemy occupies the fire location - let them burn!
std::vector<PlayerId> allyPids; // Empty for now - assume 2-player game
@@ -418,8 +407,8 @@ bool AICommandFilter::IsWastefulMovement(
PlayerId pid,
bool isDefender,
const GameStateW& gameState,
const SettingsGetter& settings,
const APDCache& apdCache,
const BattalionTypeGetter& battalionTypeGetter,
const CoordsSet& enemyLocations,
double minDistToEnemies) {
if (cmd.GetCommandType() != CommandType::MOVE_COMMAND) { return false; }
@@ -429,17 +418,17 @@ bool AICommandFilter::IsWastefulMovement(
return false; // Don't filter defender movement or when close to enemies
}
// Get unit and target information directly from command
const int unitId = cmd.GetActorUnitId();
const int targetRow = cmd.GetTargetRow();
const int targetCol = cmd.GetTargetColumn();
// Get the command proto to access unit and target information
const auto cmdProto = cmd.GetCommandProto();
// Check if we have the required information
if (unitId < 0 || targetRow < 0 || targetCol < 0) {
throw ShardokInternalErrorException(
"MOVE_COMMAND missing required actor or target information");
if (!cmdProto.has_actor() || !cmdProto.has_target()) {
return false; // Can't analyze without unit and target info
}
const auto unitId = cmdProto.actor().value();
const auto& targetCoords = cmdProto.target();
// Get the acting unit directly by ID
const Unit* actingUnit = gameState->units()->Get(unitId);
// Verify the unit is still active
@@ -455,10 +444,12 @@ bool AICommandFilter::IsWastefulMovement(
}
const auto& currentCoords = actingUnit->location();
const Coords targetCoordsFlat(static_cast<int8_t>(targetRow), static_cast<int8_t>(targetCol));
const Coords targetCoordsFlat{
static_cast<int8_t>(targetCoords.row()),
static_cast<int8_t>(targetCoords.column())};
// Get action point distances for this unit's battalion type
const auto& battType = battalionTypeGetter(actingUnit->battalion().type());
const auto& battType = settings.GetBattalionType(actingUnit->battalion().type());
const auto* apd = apdCache->GetRaw(
gameState->hex_map(),
ActionPointDistancesCache::GetMapId(gameState->hex_map()),
@@ -495,13 +486,12 @@ bool AICommandFilter::IsWastefulMovement(
}
bool AICommandFilter::IsStrategicBlunder(
const ShardokCommand& /*cmd*/,
PlayerId /*pid*/,
bool /*isDefender*/,
const GameStateW& /*gameState*/,
const APDCache& /*apdCache*/,
const BattalionTypeGetter& /*battalionTypeGetter*/,
double /*minDistToEnemies*/) {
const ShardokCommand& cmd,
PlayerId pid,
bool isDefender,
const GameStateW& gameState,
const SettingsGetter& settings,
double minDistToEnemies) {
// Simplified strategic blunder detection for now
// TODO: Implement proper castle abandonment detection
// TODO: Use minDistToEnemies for strategic blunder logic
@@ -516,8 +506,8 @@ double AICommandFilter::MinDistanceToEnemyUnits(
double minDistance = std::numeric_limits<double>::max();
const auto* units = gameState->units();
for (size_t i = 0; i < units->size(); ++i) {
const auto* playerUnit = units->Get(static_cast<unsigned int>(i));
for (int i = 0; i < units->size(); ++i) {
const auto* playerUnit = units->Get(i);
if (playerUnit->status() == net::eagle0::shardok::storage::fb::UnitStatus_NORMAL_UNIT &&
playerUnit->player_id() == pid) {
const auto& playerCoords = playerUnit->location();
@@ -547,8 +537,8 @@ double AICommandFilter::MinDistanceToCastles(
}
// Find minimum hex distance from any player unit to any castle
for (size_t i = 0; i < units->size(); ++i) {
const auto* unit = units->Get(static_cast<unsigned int>(i));
for (int i = 0; i < units->size(); ++i) {
const auto* unit = units->Get(i);
if (unit->status() == net::eagle0::shardok::storage::fb::UnitStatus_NORMAL_UNIT &&
unit->player_id() == pid) {
const auto& unitCoords = unit->location();
@@ -582,8 +572,8 @@ int AICommandFilter::CountPlayerUnits(const GameStateW& gameState, PlayerId pid)
int count = 0;
const auto* units = gameState->units();
for (size_t i = 0; i < units->size(); ++i) {
const auto* unit = units->Get(static_cast<unsigned int>(i));
for (int i = 0; i < units->size(); ++i) {
const auto* unit = units->Get(i);
if (unit->status() == net::eagle0::shardok::storage::fb::UnitStatus_NORMAL_UNIT &&
unit->player_id() == pid) {
count++;
@@ -594,9 +584,9 @@ int AICommandFilter::CountPlayerUnits(const GameStateW& gameState, PlayerId pid)
}
bool AICommandFilter::WouldAbandonCriticalCastle(
const ShardokCommand& /*cmd*/,
PlayerId /*pid*/,
const GameStateW& /*gameState*/) {
const ShardokCommand& cmd,
PlayerId pid,
const GameStateW& gameState) {
// Simplified implementation - return false for now
// TODO: Implement proper castle abandonment detection when API is available
return false;
@@ -8,12 +8,12 @@
#include <memory>
#include <vector>
#include "src/main/cpp/net/eagle0/shardok/ai/AICommonTypes.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/ShardokEngine.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/action_point_distances/ActionPointDistancesCache.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/map/CoordsSet.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/settings/GameSettings.hpp"
#include "src/main/flatbuffer/net/eagle0/shardok/storage/game_state.hpp"
#include "src/main/protobuf/net/eagle0/shardok/api/command_descriptor.pb.h"
namespace shardok {
@@ -32,8 +32,8 @@ public:
* @param pid Player ID making the move
* @param isDefender True if this player is the defender
* @param gameState Current game state
* @param settings Game settings for parameter lookup
* @param apdCache Action point distance cache for distance calculations
* @param battalionTypeLookup Function to look up battalion types by ID
* @return Filtered list of commands worth evaluating
*/
static std::vector<size_t> FilterCommands(
@@ -41,8 +41,8 @@ public:
PlayerId pid,
bool isDefender,
const GameStateW& gameState,
const APDCache& apdCache,
const BattalionTypeGetter& battalionTypeLookup);
const SettingsGetter& settings,
const APDCache& apdCache);
private:
// Helper to build enemy locations once for efficiency
@@ -54,8 +54,8 @@ private:
PlayerId pid,
bool isDefender,
const GameStateW& gameState,
const SettingsGetter& settings,
const APDCache& apdCache,
const BattalionTypeGetter& battalionTypeLookup,
const CoordsSet& enemyLocations,
const CoordsSet& castleLocations,
double minDistToEnemies);
@@ -66,8 +66,8 @@ private:
PlayerId pid,
bool isDefender,
const GameStateW& gameState,
const SettingsGetter& settings,
const APDCache& apdCache,
const BattalionTypeGetter& battalionTypeLookup,
const CoordsSet& enemyLocations,
double minDistToEnemies);
@@ -77,8 +77,7 @@ private:
PlayerId pid,
bool isDefender,
const GameStateW& gameState,
const APDCache& apdCache,
const BattalionTypeGetter& battalionTypeLookup,
const SettingsGetter& settings,
double minDistToEnemies);
// Helper functions for distance and position analysis
@@ -1,23 +0,0 @@
//
// AICommonTypes.hpp
// Common type definitions used across AI utility functions
//
#ifndef EAGLE0_AICOMMONTYPES_HPP
#define EAGLE0_AICOMMONTYPES_HPP
#include <functional>
#include "src/main/cpp/net/eagle0/shardok/library/BattalionType.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/ShardokCTypes.h"
namespace shardok {
// Function type for looking up battalion types by ID
// Used across AI utilities to get battalion type information without
// needing to pass the entire scorer object
using BattalionTypeGetter = std::function<BattalionTypeSPtr(BattalionTypeId)>;
} // namespace shardok
#endif // EAGLE0_AICOMMONTYPES_HPP
@@ -1,25 +0,0 @@
//
// AI System Types and Configuration
//
#ifndef EAGLE0_AI_CONFIG_HPP
#define EAGLE0_AI_CONFIG_HPP
namespace shardok {
// Enum for AI algorithm selection
enum class AIAlgorithmType {
ITERATIVE_DEEPENING, // Default: Minimax with sophisticated randomness
MCTS // Monte Carlo Tree Search with multithreading
};
// Enum for scoring calculator selection
enum class ScoringCalculatorType {
STANDARD, // Default: Unbounded raw scores
NORMALIZED, // Normalized scores in [0, 1] range for ML training
MCTS_OPTIMIZED // Bounded linear scores tuned for MCTS
};
} // namespace shardok
#endif // EAGLE0_AI_CONFIG_HPP
@@ -4,13 +4,8 @@
#include "AIDefenderStrategySelector.hpp"
#include <algorithm>
#include <ranges>
#include "src/main/cpp/net/eagle0/shardok/ai/AIAttackGroups.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AIScoreUtilities.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AIWaterCrossingCalculator.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/score/AIScoreCalculator.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/util/HexMapUtils.hpp"
namespace shardok {
@@ -19,11 +14,10 @@ constexpr double MAXIMUM_RATIO_FOR_DEFENDER_TO_FLEE = 0.15;
constexpr double MINIMUM_RATIO_FOR_DEFENDER_TO_HOLD = 0.60;
auto AIDefenderStrategySelector::BestDefenderStrategy(
const GameStateW& gameState,
const GameState* gameState,
const CoordsSet& criticalTileCoords,
int maxRounds,
const APDCache& apdCache,
const BattalionTypeGetter& battalionTypeGetter) -> AIStrategy {
const SettingsGetter& settings) -> AIStrategy {
uint32_t attackerNonUndeadUnitCount = 0;
uint32_t attackerNonUndeadUnitNotRequiringWaterCrossingCount = 0;
int attackerTroops = 0;
@@ -39,7 +33,7 @@ auto AIDefenderStrategySelector::BestDefenderStrategy(
player->player_id(),
criticalTileCoords,
apdCache,
battalionTypeGetter);
settings);
attackerUnitIdsRequiringWaterCrossing.insert(
attackerUnitIdsRequiringWaterCrossing.end(),
unitIdsRequiringWaterCrossing.begin(),
@@ -63,9 +57,7 @@ auto AIDefenderStrategySelector::BestDefenderStrategy(
net::eagle0::shardok::storage::fb::BattalionTypeId_UNDEAD) {
++attackerNonUndeadUnitCount;
if (!std::ranges::contains(
attackerUnitIdsRequiringWaterCrossing,
unit->unit_id())) {
if (!common::Contains(attackerUnitIdsRequiringWaterCrossing, unit->unit_id())) {
++attackerNonUndeadUnitNotRequiringWaterCrossingCount;
}
}
@@ -74,7 +66,7 @@ auto AIDefenderStrategySelector::BestDefenderStrategy(
}
}
const int roundsRemaining = maxRounds - gameState->current_round();
const int roundsRemaining = 32 - gameState->current_round();
AIStrategy chosenStrategy;
// Defender will flee if
@@ -6,23 +6,20 @@
#define EAGLE0_AIDEFENDERSTRATEGYSELECTOR_HPP
#include "src/main/cpp/net/eagle0/shardok/ai/AIAttackLocations.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AICommonTypes.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AIStrategy.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/GameStateW.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/action_point_distances/ActionPointDistancesCache.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/map/CoordsSet.hpp"
#include "src/main/flatbuffer/net/eagle0/shardok/storage/game_state.hpp"
namespace shardok {
class AIDefenderStrategySelector {
using GameState = net::eagle0::shardok::storage::fb::GameState;
public:
static auto BestDefenderStrategy(
const GameStateW& gameState,
const GameState* gameState,
const CoordsSet& criticalTileCoords,
int maxRounds,
const APDCache& apdCache,
const BattalionTypeGetter& battalionTypeGetter) -> AIStrategy;
const SettingsGetter& settings) -> AIStrategy;
};
} // namespace shardok
@@ -49,8 +49,8 @@ auto DefenderDistanceBuf(
const vector<const Unit *> &attackerUnits,
const APDCache &apdCache,
const ALCache &alCache,
const BattalionTypeGetter &battalionTypeGetter,
ActionPoints braveWaterCost,
const SettingsGetter &settings,
const int braveWaterActionPointCost,
const bool lateGame,
const bool includeUndead) -> double {
const auto &locationsToAttackMe = alCache->CachedLocations(defenderLocation, lateGame);
@@ -73,14 +73,14 @@ auto DefenderDistanceBuf(
notBravingDistances[typeInt] = apdCache->GetRaw(
hexMap,
mapId,
battalionTypeGetter(attacker->battalion().type()),
settings.GetBattalionType(attacker->battalion().type()),
false);
bravingDistances[typeInt] = apdCache->GetRaw(
hexMap,
mapId,
battalionTypeGetter(attacker->battalion().type()),
settings.GetBattalionType(attacker->battalion().type()),
true,
braveWaterCost);
braveWaterActionPointCost);
}
}
@@ -6,10 +6,10 @@
#define EAGLE0_AIDISTANCEDEBUF_HPP
#include "AIAttackLocations.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AICommonTypes.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/action_point_distances/ActionPointDistances.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/action_point_distances/ActionPointDistancesCache.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/map/CoordsSet.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/settings/GameSettings.hpp"
namespace shardok {
@@ -23,8 +23,8 @@ auto DefenderDistanceBuf(
const vector<const Unit *> &attackerUnits,
const APDCache &apdCache,
const ALCache &alCache,
const BattalionTypeGetter &battalionTypeGetter,
ActionPoints braveWaterCost,
const SettingsGetter &settings,
int braveWaterActionPointCost,
bool lateGame,
bool includeUndead) -> double;
@@ -9,31 +9,23 @@
#include "AIFleeDecisionCalculator.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AIScoreUtilities.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AIUnitScoreCalculator.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/util/HexMapUtils.hpp"
namespace shardok {
auto AIFleeDecisionCalculator::GetFleeCommandIndex(
const CommandList::const_iterator& fleeCommand,
const CommandListSPtr& availableCommands) -> size_t {
return static_cast<size_t>(std::distance(availableCommands->begin(), fleeCommand));
const vector<CommandProto>::const_iterator& fleeCommand,
const vector<CommandProto>& availableCommands) -> size_t {
return static_cast<size_t>(std::distance(availableCommands.begin(), fleeCommand));
}
auto AIFleeDecisionCalculator::EstimateCombatSuccess(
PlayerId attackerPlayerId,
const GameStateW& gameState,
int maxRounds) -> double {
if (gameState->status() == nullptr ||
gameState->status()->state() !=
net::eagle0::shardok::storage::fb::GameStatus_::State_GAME_RUNNING) {
return 1.0; // we're still in set_up so we can't really evaluate
}
const GameStateW& guessedState,
const GameSettingsSPtr& settings) -> double {
// Combat success estimation based on troops, heroes, and capture dynamics
// Combat success estimation based on unit power, heroes, and capture dynamics
double attackerPower = 0.0;
double defenderPower = 0.0;
int attackerTroops = 0; // Still track raw troops for special cases
int attackerTroops = 0;
int defenderTroops = 0;
int attackerUnits = 0;
int defenderUnits = 0;
@@ -41,19 +33,17 @@ auto AIFleeDecisionCalculator::EstimateCombatSuccess(
int defenderHeroes = 0;
bool defenderHasVips = false;
// Calculate total power and count units/heroes for each side
for (const auto* unit : *gameState->units()) {
// Count troops, units, and heroes for each side
for (const auto* unit : *guessedState->units()) {
if (unit->status() != net::eagle0::shardok::storage::fb::UnitStatus_NORMAL_UNIT) continue;
const auto* pi = PlayerInfoForPid(gameState, unit->player_id());
const auto* pi = PlayerInfoForPid(guessedState, unit->player_id());
if (pi == nullptr) continue;
const int unitTroops = unit->battalion().size();
const bool hasHero = unit->has_attached_hero();
const double unitPower = ContextFreeUnitValue(unit);
if (pi->is_defender()) {
defenderPower += unitPower;
defenderTroops += unitTroops;
defenderUnits++;
if (hasHero) {
@@ -61,50 +51,46 @@ auto AIFleeDecisionCalculator::EstimateCombatSuccess(
if (unit->attached_hero().is_vip()) { defenderHasVips = true; }
}
} else if (unit->player_id() == attackerPlayerId) {
attackerPower += unitPower;
attackerTroops += unitTroops;
attackerUnits++;
if (hasHero) { attackerHeroes++; }
}
}
const int roundsRemaining = maxRounds - gameState->current_round();
const int roundsRemaining =
settings->GetGetter().Backing().max_rounds() - guessedState->current_round();
// Special case: Attacker has no heroes - automatic loss
if (attackerHeroes == 0) {
return 0.0; // Cannot win without heroes
}
// Special case: Defender has no heroes - automatic win for attacker
if (defenderHeroes == 0) {
return 1.0; // Guaranteed win
}
// Special case: Attacker has no troops (but has heroes)
// Special case: Attacker has no troops
if (attackerTroops == 0) {
// Very difficult to win with heroes alone
return 0.05; // Extremely low chance
// Even with heroes, attacker is extremely unlikely to win without troops
return 0.01; // Near zero, but not absolute zero
}
// Special case: Defender has no troops but has heroes
if (defenderTroops == 0) {
// Defenders with only heroes are vulnerable to capture
// Only truly difficult if time is extremely limited
if (roundsRemaining <= 1) {
// Last round - very hard to capture all heroes
return 0.3; // Low but not impossible
} else if (roundsRemaining <= 2) {
return 0.6; // Still achievable
if (defenderTroops == 0 && defenderHeroes > 0) {
// Defender can win by running out the clock if attacker can't capture heroes
// Success depends heavily on remaining time and attacker's ability to capture
if (roundsRemaining <= 3) {
// Very hard for attacker to capture all heroes in time
return 0.15; // Low chance
} else if (roundsRemaining <= 5) {
return 0.25; // Still difficult
} else {
// With 3+ rounds, capturing defenseless heroes is quite feasible
return 0.85; // High probability of success
// More time available, but still challenging
return 0.35;
}
}
// Special case: Defender has neither troops nor heroes
if (defenderTroops == 0 && defenderHeroes == 0) {
return 0.95; // Nearly guaranteed win
}
// Normal case: Both sides have troops
// Base probability from power ratio (accounts for unit quality, not just quantity)
const double powerRatio = attackerPower / std::max(1.0, defenderPower);
double baseProbability = std::min(0.95, std::max(0.05, powerRatio * 0.5));
// Base probability from troop ratio
const double troopRatio =
static_cast<double>(attackerTroops) / static_cast<double>(defenderTroops);
double baseProbability = std::min(0.95, std::max(0.05, troopRatio * 0.5));
// Adjust for time pressure - attackers need to win before time runs out
if (roundsRemaining <= 1) {
@@ -133,15 +119,18 @@ auto AIFleeDecisionCalculator::EstimateCombatSuccess(
auto AIFleeDecisionCalculator::EvaluateFleeVsFight(
PlayerId playerId,
const GameSettingsSPtr& settings,
const GameStateW& guessedState,
const CommandListSPtr& availableCommands,
const CommandList::const_iterator& fleeCommand,
int maxRounds,
int minimumFleeOddsThreshold,
int desperateFleeThreshold,
const vector<CommandProto>& availableCommands,
const vector<CommandProto>::const_iterator& fleeCommand,
bool enableDebugLogging) -> FleeDecision {
// Get flee success odds
const int fleeSuccessChance = (*fleeCommand)->GetOddsPercentile();
const int fleeSuccessChance = fleeCommand->odds().success_chance();
// Get thresholds from settings
const auto settingsGetter = settings->GetGetter();
const int minimumFleeOddsThreshold = settingsGetter.Backing().ai_minimum_flee_odds_threshold();
const int desperateFleeThreshold = settingsGetter.Backing().ai_desperate_flee_threshold();
if (enableDebugLogging) {
printf("AI FinalRound: Evaluating flee (odds=%d%%)...\n", fleeSuccessChance);
@@ -161,10 +150,10 @@ auto AIFleeDecisionCalculator::EvaluateFleeVsFight(
}
// Low flee odds - evaluate if fighting might be better
const double combatWinChance = EstimateCombatSuccess(playerId, guessedState, maxRounds);
const double combatWinChance = EstimateCombatSuccess(playerId, guessedState, settings);
// If combat situation is hopeless, even bad flee odds are better than certain death
if (combatWinChance <= 0.05 && fleeSuccessChance >= desperateFleeThreshold) {
if (combatWinChance < 0.05 && fleeSuccessChance >= desperateFleeThreshold) {
if (enableDebugLogging) {
printf("AI FinalRound: Combat hopeless (%.1f%%), desperate flee attempt (%d%%)\n",
combatWinChance * 100,
@@ -210,17 +199,4 @@ auto AIFleeDecisionCalculator::EvaluateFleeVsFight(
}
}
auto AIFleeDecisionCalculator::ShouldConsiderFleeing(
PlayerId attackerPlayerId,
const GameStateW& guessedState,
int maxRounds,
double fleeConsiderationThreshold) -> bool {
// Get combat success probability
const double combatSuccessChance =
EstimateCombatSuccess(attackerPlayerId, guessedState, maxRounds);
// Consider fleeing if combat success chance is below threshold
return combatSuccessChance < fleeConsiderationThreshold;
}
} // namespace shardok
@@ -9,11 +9,14 @@
#ifndef AIFleeDecisionCalculator_hpp
#define AIFleeDecisionCalculator_hpp
#include "src/main/cpp/net/eagle0/shardok/library/ShardokCommand.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/ShardokEngine.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/settings/GameSettings.hpp"
#include "src/main/protobuf/net/eagle0/shardok/api/command_descriptor.pb.h"
namespace shardok {
using CommandProto = net::eagle0::shardok::api::CommandDescriptor;
class AIFleeDecisionCalculator {
public:
// Configuration for flee decision thresholds
@@ -32,33 +35,23 @@ public:
// Evaluate whether to flee or fight in the final round
[[nodiscard]] static auto EvaluateFleeVsFight(
PlayerId playerId,
const GameSettingsSPtr& settings,
const GameStateW& guessedState,
const CommandListSPtr& availableCommands,
const CommandList::const_iterator& fleeCommand,
int maxRounds,
int minimumFleeOddsThreshold,
int desperateFleeThreshold,
const vector<CommandProto>& availableCommands,
const vector<CommandProto>::const_iterator& fleeCommand,
bool enableDebugLogging = false) -> FleeDecision;
// Estimate probability of combat success for the attacker
[[nodiscard]] static auto EstimateCombatSuccess(
PlayerId attackerPlayerId,
const GameStateW& guessedState,
int maxRounds) -> double;
// Determine if the attacker should consider fleeing based on combat odds
// Returns true if fleeing should be considered as an option
[[nodiscard]] static auto ShouldConsiderFleeing(
PlayerId attackerPlayerId,
const GameStateW& guessedState,
int maxRounds,
double fleeConsiderationThreshold = 0.5) -> bool;
const GameSettingsSPtr& settings) -> double;
private:
// Helper to get flee command index
[[nodiscard]] static auto GetFleeCommandIndex(
const CommandList::const_iterator& fleeCommand,
const CommandListSPtr& availableCommands) -> size_t;
const vector<CommandProto>::const_iterator& fleeCommand,
const vector<CommandProto>& availableCommands) -> size_t;
};
} // namespace shardok
@@ -1,251 +0,0 @@
//
// Fast heuristic weighting implementation with context-aware logic
//
#include "AIHeuristicWeighting.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/ShardokException.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/action_point_distances/ActionPointDistances.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/util/HexMapUtils.hpp"
namespace shardok {
using CommandType = net::eagle0::shardok::common::CommandType;
using Coords = net::eagle0::shardok::storage::fb::Coords;
using ProtoCoords = net::eagle0::shardok::common::Coords;
double AIHeuristicWeighting::GetCommandWeight(
const CommandType commandType,
const UnitId actorUnitId,
const PlayerId actorPlayerId,
const Coords& targetCoords,
const GameStateW& state,
const CoordsSet& castleCoords,
const APDCache* apdCache,
bool isDefender,
std::function<BattalionTypeSPtr(BattalionTypeId)> getBattalionType) {
// Fast O(1) heuristic weights based on command type and game context
// Higher weight = more likely to select in simulation
// 0.0 = never select (filtered out)
const auto* hexMap = state->hex_map();
const auto* units = state->units();
const bool hasTarget = (targetCoords.row() >= 0 && targetCoords.column() >= 0);
switch (commandType) {
// === HIGH VALUE OFFENSIVE (10.0) ===
// Ranged attacks - very valuable, typically available when in range
case CommandType::ARCHERY_COMMAND: return 20.0;
case CommandType::LIGHTNING_BOLT_COMMAND: return 10.0;
case CommandType::FEAR_COMMAND: return 10.0;
// Area/tactical spells - high impact
case CommandType::METEOR_START_COMMAND: {
// METEOR_START doesn't have a target - it's based on actor location
if (hasTarget) {
throw ShardokInternalErrorException(
"METEOR_START_COMMAND should not have target coordinates");
}
// Get actor's location
const auto* actorUnit = units->Get(actorUnitId);
if (!actorUnit) {
throw ShardokInternalErrorException(
"METEOR_START_COMMAND actor unit not found in game state");
}
const Coords& actorLocation = actorUnit->location();
int enemyCount = 0;
// Count enemies within meteor range (3 hexes) of actor location
constexpr int METEOR_RANGE = 3;
const auto tilesInRange = TilesWithinDistance(hexMap, actorLocation, METEOR_RANGE);
for (const auto& tileCoords : tilesInRange) {
if (const auto* unit = Occupant(units, tileCoords)) {
if (unit->player_id() != actorPlayerId) { enemyCount++; }
}
}
return 1.0 + (enemyCount * 15.0); // Base 1 + 15 per enemy in range
}
case CommandType::METEOR_TARGET_COMMAND: {
// High weight per enemy unit at or adjacent to target
if (!hasTarget) {
throw ShardokInternalErrorException(
"METEOR_TARGET_COMMAND requires target coordinates for heuristic "
"weighting");
}
int enemyCount = 0;
// Count enemies at target
if (const auto* targetUnit = Occupant(units, targetCoords)) {
if (targetUnit->player_id() != actorPlayerId) { enemyCount++; }
}
// Count enemies adjacent to target
for (const auto& neighbor : HexMapUtils::GetAdjacentTiles(hexMap, targetCoords)) {
if (const auto* unit = Occupant(units, neighbor.coords)) {
if (unit->player_id() != actorPlayerId) { enemyCount++; }
}
}
return 1.0 + (enemyCount * 15.0); // Base 1 + 15 per enemy in range
}
case CommandType::RAISE_DEAD_COMMAND: return 10.0;
case CommandType::HOLY_WAVE_COMMAND: return 8.0;
// Fire on enemy (context-dependent)
case CommandType::START_FIRE_COMMAND: {
// High if enemy at target, low otherwise
if (!hasTarget) {
throw ShardokInternalErrorException(
"START_FIRE_COMMAND requires target coordinates for heuristic weighting");
}
if (const auto* targetUnit = Occupant(units, targetCoords)) {
if (targetUnit->player_id() != actorPlayerId) {
return 10.0; // Enemy at target - high value
}
}
return 1.0; // No enemy - low value but still valid
}
// === MEDIUM-HIGH OFFENSIVE (5.0-7.0) ===
// Direct damage melee
case CommandType::MELEE_COMMAND: return 7.0;
case CommandType::CHARGE_COMMAND: return 7.0; // Damage + movement
case CommandType::CHALLENGE_DUEL_COMMAND: return 5.0;
// Control and tactical magic
case CommandType::CONTROL_COMMAND: return 6.0;
case CommandType::METEOR_CAST_COMMAND: return 6.0; // Finish meteor
case CommandType::REDUCE_COMMAND: {
// High if enemy at target, zero otherwise
if (!hasTarget) return 0.0;
if (const auto* targetUnit = Occupant(units, targetCoords)) {
if (targetUnit->player_id() != actorPlayerId) {
return 10.0; // Enemy at target - very high value
}
}
return 0.0; // No enemy - don't use
}
// === MOVEMENT - Context-dependent ===
case CommandType::MOVE_COMMAND: {
if (isDefender) {
return 0.0; // Defenders don't move
}
// Attackers: weight based on distance improvement towards castle
if (!hasTarget) {
throw ShardokInternalErrorException(
"MOVE_COMMAND requires target coordinates for heuristic weighting");
}
// Get actor unit to determine battalion type and start position
const auto* actorUnit = units->Get(actorUnitId);
if (!actorUnit) return 4.0; // Default if can't find actor
// Get battalion type for distance calculation
const auto battalionTypeId = actorUnit->battalion().type();
const auto battalionTypePtr = getBattalionType(battalionTypeId);
if (!battalionTypePtr) return 4.0; // Default if can't get battalion type
// Get ActionPointDistances for this battalion type
const auto mapId = ActionPointDistancesCache::GetMapId(hexMap);
const auto* apd = (*apdCache)->GetRaw(hexMap, mapId, battalionTypePtr, false, -1);
if (!apd) return 4.0; // Default if can't get distances
// Calculate minimum distance from start to any castle
const Coords startCoords = actorUnit->location();
auto minStartDistance = ActionPointDistances::IMPOSSIBLE;
for (const auto& castleCoord : castleCoords) {
const auto dist = apd->Distance(startCoords, castleCoord);
if (dist < minStartDistance) { minStartDistance = dist; }
}
// Calculate minimum distance from end to any castle
const Coords& endCoords = targetCoords;
auto minEndDistance = ActionPointDistances::IMPOSSIBLE;
for (const auto& castleCoord : castleCoords) {
const auto dist = apd->Distance(endCoords, castleCoord);
if (dist < minEndDistance) { minEndDistance = dist; }
}
// Return weight based on distance improvement
// Higher weight if we're moving closer to castle
if (minStartDistance == ActionPointDistances::IMPOSSIBLE ||
minEndDistance == ActionPointDistances::IMPOSSIBLE) {
return 4.0; // Default if distances are impossible
}
const auto improvement = static_cast<double>(minStartDistance - minEndDistance);
return std::max(0.0, improvement);
}
case CommandType::BRAVE_WATER_COMMAND: return 3.0; // Tactical movement
case CommandType::SCOUT_COMMAND:
return 2.0; // Information gathering
// Terrain manipulation
case CommandType::FREEZE_WATER_COMMAND: return 3.0;
case CommandType::BUILD_BRIDGE_COMMAND: return 3.0;
// === LOW VALUE DEFENSIVE/UTILITY (1.0-2.0) ===
case CommandType::EXTINGUISH_FIRE_COMMAND: {
// High if friendly at target, low otherwise
if (!hasTarget) {
throw ShardokInternalErrorException(
"EXTINGUISH_FIRE_COMMAND requires target coordinates for heuristic "
"weighting");
}
if (const auto* targetUnit = Occupant(units, targetCoords)) {
if (targetUnit->player_id() == actorPlayerId) {
return 8.0; // Friendly at target - high value
}
}
return 1.0; // No friendly - low value but still valid
}
case CommandType::UNIT_REST_COMMAND: return 1.5;
case CommandType::FORTIFY_COMMAND: return 2.0;
// Zero weight - don't use in simulation
case CommandType::REPAIR_COMMAND: return 0.0;
case CommandType::HIDE_COMMAND: return 0.0;
case CommandType::RELEASE_UNIT_COMMAND: return 0.0;
case CommandType::REINFORCE_COMMAND: return 10.0;
case CommandType::MANAGE_PRISONER: return 1.0;
// === ZERO WEIGHT - NEVER SELECT (0.0) ===
// Explicitly bad actions
case CommandType::FLEE_COMMAND: return 0.0; // Never flee in simulation
case CommandType::RETREAT_COMMAND: return 0.0;
case CommandType::BECOME_OUTLAW_COMMAND: return 0.0; // Never become outlaw
case CommandType::DISMISS_UNIT_COMMAND:
return 0.0; // Never dismiss in combat
// Actions that are fine as a fallback
case CommandType::END_TURN_COMMAND: return 1.0;
case CommandType::UNIT_STOP_COMMAND: return 1.0;
case CommandType::METEOR_CANCEL_COMMAND: return 1.0;
// Setup commands (shouldn't appear in combat, but filter anyway)
case CommandType::PLACE_UNIT_COMMAND: return 10.0;
case CommandType::PLACE_HIDDEN_UNIT_COMMAND: return 1.0;
case CommandType::END_PLAYER_SETUP_COMMAND: return 1.0;
// Unknown/unhandled
case CommandType::UNKNOWN_COMMAND:
default: return 0.0; // Don't select unknown commands
}
}
} // namespace shardok
@@ -1,40 +0,0 @@
//
// Fast heuristic weighting for MCTS simulations
// Provides O(1) weights based on command type and context
//
#ifndef EAGLE0_AI_HEURISTIC_WEIGHTING_HPP
#define EAGLE0_AI_HEURISTIC_WEIGHTING_HPP
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Wdeprecated-redundant-constexpr-static-def"
#include "src/main/protobuf/net/eagle0/shardok/common/command_type.pb.h"
#pragma clang diagnostic pop
#include "src/main/cpp/net/eagle0/shardok/library/GameStateW.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/action_point_distances/ActionPointDistancesCache.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/map/CoordsSet.hpp"
namespace shardok {
// Fast heuristic-based command weighting for MCTS simulation policy
// Avoids expensive score calculation while maintaining intelligent bias
class AIHeuristicWeighting {
public:
// Get weight for a command using fast heuristics with game context
// Returns weight >= 0.0, where 0.0 means "never select" and higher is more likely
static double GetCommandWeight(
net::eagle0::shardok::common::CommandType commandType,
UnitId actorUnitId,
PlayerId actorPlayerId,
const Coords& targetCoords,
const GameStateW& state,
const CoordsSet& castleCoords,
const APDCache* apdCache,
bool isDefender,
std::function<BattalionTypeSPtr(BattalionTypeId)> getBattalionType);
};
} // namespace shardok
#endif // EAGLE0_AI_HEURISTIC_WEIGHTING_HPP
File diff suppressed because it is too large Load Diff
@@ -0,0 +1,169 @@
//
// Created by dancrosby on 3/4/20.
//
#ifndef EAGLE0_AISCORECALCULATOR_HPP
#define EAGLE0_AISCORECALCULATOR_HPP
#include <future>
#include "src/main/cpp/net/eagle0/shardok/ai/AIAttackLocations.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AIStrategy.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/ShardokCTypes.h"
#include "src/main/cpp/net/eagle0/shardok/library/ShardokEngine.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/action_point_distances/ActionPointDistancesCache.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/settings/GameSettings.hpp"
#include "src/main/flatbuffer/net/eagle0/shardok/storage/game_state.hpp"
#include "src/main/protobuf/net/eagle0/shardok/api/command_descriptor.pb.h"
#include "src/main/protobuf/net/eagle0/shardok/api/game_state_view.pb.h"
namespace shardok {
using net::eagle0::shardok::api::GameStateView;
using GameState = fb::GameState;
using shardok::PlayerId;
using std::future;
using std::vector;
using ScoreValue = double;
using CommandProto = net::eagle0::shardok::api::CommandDescriptor;
class AIScoreCalculator {
public:
struct IndexAndScore {
size_t index;
CommandType type;
ScoreValue lookaheadScore;
ScoreValue immediateScore;
};
private:
[[nodiscard]] static auto DefenderScatterStrategyScoreForState(
const GameStateW &gameState,
int roundsRemaining,
const SettingsGetter &settings,
const ALCache &alCache,
const APDCache &apdCache) -> ScoreValue;
[[nodiscard]] static auto DefenderHoldCastlesStrategyScoreForState(
const GameStateW &gameState,
const CoordsSet &castleCoords,
int roundsRemaining,
const SettingsGetter &settings,
const ALCache &alCache,
const APDCache &apdCache) -> ScoreValue;
[[nodiscard]] static auto FleeStrategyScoreForState(
const GameStateW &gameState,
PlayerId playerId) -> ScoreValue;
[[nodiscard]] static auto DefenderScoreForState(
const GameStateW &gameState,
const AIStrategy &defenderStrategy,
const CoordsSet &castleCoords,
int roundsRemaining,
const SettingsGetter &settings,
const ALCache &alCache,
const APDCache &apdCache) -> ScoreValue;
[[nodiscard]] static auto AttackerScoreForState(
const GameStateW &gameState,
const AIStrategy &attackerStrategy,
const CoordsSet &castleCoords,
int roundsRemaining,
const SettingsGetter &settings,
const ALCache &alCache,
const APDCache &apdCache) -> ScoreValue;
struct ImmediateAndLookaheadScore {
ScoreValue immediateScore;
future<ScoreValue> lookaheadScore;
};
static auto BasicLookaheadCalculator(
PlayerId pid,
bool isDefender,
int remainingLookahead,
int maxRepeatCount,
const shared_ptr<ShardokEngine> &innerEngine,
ScoreValue currentUtility,
const AIStrategy &attackerStrategy,
const SettingsGetter &settingsGetter,
const CoordsSet &allCastleCoords,
const APDCache &apdCache,
const ALCache &alCache) -> ScoreValue;
static auto CalcOne(
PlayerId pid,
bool isDefender,
uint32_t commandIndex,
int remainingLookahead,
int maxRepeatCount,
const std::shared_ptr<RandomGenerator> &randomGenerator,
const ShardokEngine &guessedEngine,
const AIStrategy &attackerStrategy,
const SettingsGetter &settingsGetter,
const CoordsSet &allCastleCoords,
const APDCache &apdCache,
const ALCache &alCache) -> ImmediateAndLookaheadScore;
struct CommandEvaluationResult {
ScoreValue immediateScore;
ScoreValue lookaheadScore;
};
static auto EvaluateCommand(
PlayerId pid,
bool isDefender,
uint32_t commandIndex,
int remainingLookahead,
int maxRepeatCount,
const ShardokEngine &guessedEngine,
const AIStrategy &attackerStrategy,
ScoreValue currentUtility,
const SettingsGetter &settingsGetter,
const CoordsSet &allCastleCoords,
const APDCache &apdCache,
const ALCache &alCache) -> CommandEvaluationResult;
public:
[[nodiscard]] static auto GuessedStateScore(
bool isDefender,
const GameStateW &state,
const AIStrategy &aiStrategy,
const CoordsSet &allCastleCoords,
const SettingsGetter &settingsGetter,
const APDCache &apdCache,
const ALCache &alCache) -> ScoreValue;
[[nodiscard]] static auto BestCommandIndex(
PlayerId pid,
bool isDefender,
int remainingLookahead,
int maxRepeatCount,
const ShardokEngine &guessedEngine,
const AIStrategy &attackerStrategy,
ScoreValue currentUtility,
const SettingsGetter &settingsGetter,
const CoordsSet &allCastleCoords,
const APDCache &apdCache,
const ALCache &alCache) -> IndexAndScore;
[[nodiscard]] static auto CommandScore(
PlayerId pid,
bool isDefender,
int remainingLookahead,
int maxRepeatCount,
const ShardokEngine &guessedEngine,
const AIStrategy &attackerStrategy,
ScoreValue currentUtility,
const SettingsGetter &settingsGetter,
const CoordsSet &allCastleCoords,
const APDCache &apdCache,
const ALCache &alCache,
size_t commandIndex) -> ScoreValue;
};
} // namespace shardok
#endif // EAGLE0_AISCORECALCULATOR_HPP
@@ -16,7 +16,7 @@ auto HasAttachedHeroWithProfession(
unit->attached_hero().profession_info().profession() == profession;
}
auto CastleClaimCapableAttackerUnitCount(const GameStateW &gameState) -> int {
auto CastleClaimCapableAttackerUnitCount(const GameState *gameState) -> int {
int count = 0;
for (const auto *unit : *gameState->units()) {
@@ -32,7 +32,7 @@ auto CastleClaimCapableAttackerUnitCount(const GameStateW &gameState) -> int {
return count;
}
auto PlayerInfoForPid(const GameStateW &gs, const PlayerId pid) -> const PlayerInfo * {
auto PlayerInfoForPid(const GameState *gs, const PlayerId pid) -> const PlayerInfo * {
if (gs->player_infos()) {
for (const auto &pi : *gs->player_infos()) {
if (pi->player_id() == pid) return pi;
@@ -7,7 +7,6 @@
#include <vector>
#include "src/main/cpp/net/eagle0/shardok/library/GameStateW.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/ShardokCTypes.h"
#include "src/main/flatbuffer/net/eagle0/shardok/storage/game_state.hpp"
#include "src/main/flatbuffer/net/eagle0/shardok/storage/unit.hpp"
@@ -26,8 +25,8 @@ auto HasAttachedHeroWithProfession(
const Unit *unit,
net::eagle0::shardok::storage::fb::Profession profession) -> bool;
auto CastleClaimCapableAttackerUnitCount(const GameStateW &gameState) -> int;
auto PlayerInfoForPid(const GameStateW &, PlayerId pid) -> const PlayerInfo *;
auto CastleClaimCapableAttackerUnitCount(const GameState *gameState) -> int;
auto PlayerInfoForPid(const GameState *gs, PlayerId pid) -> const PlayerInfo *;
} // namespace shardok
@@ -3,7 +3,6 @@
//
#include "src/main/cpp/net/eagle0/shardok/ai/AIStrategy.hpp"
namespace shardok {
AIStrategy FleeStrategy = AIStrategy{AIStrategy::STRATEGY_FLEE};
AIStrategy HoldCastlesStrategy = AIStrategy{AIStrategy::STRATEGY_HOLD_CASTLES};
@@ -24,46 +24,16 @@ int AIEvaluationCounter::GetCurrentCount() { return activeCount.load(); }
auto CalculateTimeBudget(
const PlayerId playerId,
const GameSettingsSPtr &settings,
const GameStateW &state,
const size_t numCommands) -> AITimeBudget {
const GameStateW &state) -> AITimeBudget {
const auto settingsGetter = settings->GetGetter();
const auto castleCoords = AllCastleCoords(state->hex_map());
// Check if we're in setup phase
const bool isSetupPhase = state->status()->state() ==
net::eagle0::shardok::storage::fb::GameStatus_::State_SET_UP;
// Get maximum budget cap from settings (in seconds)
const double maxBudgetSeconds =
settingsGetter.Backing().lookahead_time_budget_maximum_seconds();
const double maxBudgetMs = maxBudgetSeconds * 1000.0;
// During setup, use the setup-specific time budget
if (isSetupPhase) {
// Dynamic budget: msPerCommand × numCommands
const double msPerCommand =
settingsGetter.Backing().lookahead_time_budget_per_command_setup_ms();
const double budgetMs = msPerCommand * static_cast<double>(numCommands);
// Clamp to reasonable bounds: 200ms minimum, maxBudgetMs maximum
const auto clampedBudgetMs = std::clamp(budgetMs, 200.0, maxBudgetMs);
const auto remainingBudget =
std::chrono::milliseconds(static_cast<int64_t>(clampedBudgetMs));
const size_t minDepth = settingsGetter.Backing().min_lookahead_turns();
return AITimeBudget{
.remainingBudget = remainingBudget,
.minDepthRequired = minDepth,
.isCloseToEnemy = false}; // Not relevant during setup
}
// Determine proximity (≤4 hex distance) - applies to both attackers and defenders
bool isClose = false;
const auto *units = state->units();
for (size_t i = 0; i < units->size() && !isClose; ++i) {
const auto *myUnit = units->Get(static_cast<unsigned int>(i));
for (int i = 0; i < units->size() && !isClose; ++i) {
const auto *myUnit = units->Get(i);
if (myUnit->player_id() != playerId) continue;
const auto &myCoords = myUnit->location();
@@ -73,8 +43,8 @@ auto CalculateTimeBudget(
const Cube myCube = OffsetToCube(myCoords);
// Check distance to enemy units
for (size_t j = 0; j < units->size(); ++j) {
const auto *enemyUnit = units->Get(static_cast<unsigned int>(j));
for (int j = 0; j < units->size(); ++j) {
const auto *enemyUnit = units->Get(j);
if (enemyUnit->player_id() == playerId) continue;
const auto &enemyCoords = enemyUnit->location();
@@ -102,28 +72,15 @@ auto CalculateTimeBudget(
}
}
// Get time budget from settings - dynamic based on number of commands
// Dynamic budget: msPerCommand × numCommands
const double msPerCommand =
isClose ? settingsGetter.Backing().lookahead_time_budget_per_command_close_ms()
: settingsGetter.Backing().lookahead_time_budget_per_command_far_ms();
const double budgetMs = msPerCommand * static_cast<double>(numCommands);
// Get time budget from settings
const auto budget = std::chrono::duration<double>(
isClose ? settingsGetter.Backing().lookahead_time_budget_close_in_seconds()
: settingsGetter.Backing().lookahead_time_budget_far_in_seconds());
// Clamp to reasonable bounds: 200ms minimum, maxBudgetMs maximum
const auto clampedBudgetMs = std::clamp(budgetMs, 200.0, maxBudgetMs);
const auto remainingBudget = std::chrono::milliseconds(static_cast<int64_t>(clampedBudgetMs));
// TEMPORARY DEBUG OUTPUT
printf("[DEBUG CalculateTimeBudget] numCommands=%zu, msPerCommand=%.2f, budgetMs=%.2f, "
"clampedBudgetMs=%.2f, isClose=%d\n",
numCommands,
msPerCommand,
budgetMs,
clampedBudgetMs,
isClose);
const auto remainingBudget = std::chrono::duration_cast<std::chrono::milliseconds>(budget);
// Get minimum depth requirement
const size_t minDepth = settingsGetter.Backing().min_lookahead_turns();
const int minDepth = settingsGetter.Backing().min_lookahead_turns();
return AITimeBudget{
.remainingBudget = remainingBudget,
@@ -31,18 +31,15 @@ public:
// Configuration structure for iterative deepening time budget
struct AITimeBudget {
std::chrono::milliseconds remainingBudget; // Time budget remaining (decremented as used)
size_t minDepthRequired; // Minimum depth from minLookaheadTurns
int minDepthRequired; // Minimum depth from minLookaheadTurns
bool isCloseToEnemy; // Proximity flag for budget selection
};
// Calculate time budget based on proximity to enemies and castles
// Time budget is calculated dynamically based on number of available commands:
// budget = msPerCommand × numCommands (clamped to 200-5000ms)
auto CalculateTimeBudget(
PlayerId playerId,
const GameSettingsSPtr &settings,
const GameStateW &state,
size_t numCommands) -> AITimeBudget;
const GameStateW &state) -> AITimeBudget;
} // namespace shardok
@@ -5,7 +5,6 @@
#include "AIUnitScoreCalculator.hpp"
#include <algorithm>
#include <cstdlib>
#include "AIAttackLocations.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/util/HexMapUtils.hpp"
@@ -17,10 +16,9 @@ using std::end;
using std::shared_ptr;
constexpr double kProfessionValue = 200;
constexpr double kVigorScoreMultiplier = 5.0;
constexpr double kCastleMultiplierBonus = 1.0;
constexpr double kOnFireMultiplier = 0.25;
constexpr double kAdjacentFireMultiplier = 0.80;
constexpr double kAdjacentFireMultiplier = 0.99;
constexpr double kOnIceMultiplier = 0.25;
constexpr double kMeteorStartInRangeValue = 50;
constexpr double kMeteorDirectTargetingEnemy = 2;
@@ -64,8 +62,7 @@ auto ContextFreeUnitValue(const Unit *unit) -> ScoreValue {
4.0;
}
const double vigorValue =
unit->has_attached_hero() ? unit->attached_hero().vigor() * kVigorScoreMultiplier : 0.0;
const double vigorValue = unit->has_attached_hero() ? unit->attached_hero().vigor() : 0.0;
double battalionTypeMultiplier = 1.0;
switch (unit->battalion().type()) {
@@ -91,8 +88,8 @@ auto ContextFreeUnitValue(const Unit *unit) -> ScoreValue {
break;
}
const double battalionValue = battalionTypeMultiplier * (1.0 + armament / 100.0) *
(1.0 + training / 100.0) * (0.5 + morale / 100.0) *
const double battalionValue = battalionTypeMultiplier * (0.5 + armament / 100.0) *
(0.5 + training / 100.0) * (0.5 + morale / 100.0) *
unit->battalion().size();
const double heroValue =
@@ -101,7 +98,7 @@ auto ContextFreeUnitValue(const Unit *unit) -> ScoreValue {
return battalionValue + heroValue;
}
auto archeryValue(const Unit * /*unit*/) -> double {
auto archeryValue(const Unit *unit) -> double {
// TODO: make this depend on the value of the targets
return kArcheryPossibleValue;
}
@@ -116,7 +113,7 @@ auto reduceValue(const Unit *unit, const Terrain *unitTerrain) -> double {
return 0.0;
}
auto fearValue(const Unit * /*unit*/) -> double {
auto fearValue(const Unit *unit) -> double {
// TODO: make this depend on the value of the targets
return kFearPossibleValue;
}
@@ -337,8 +334,7 @@ auto UnitValue(
const AttackLocations &locationsThisSideCanAttackFrom,
const CoordsSet &locationsInDangerFromEnemy,
const ActionPointDistances *distances,
int meteorRange,
double meteorCastVigorCost) -> ScoreValue {
const SettingsGetter &settings) -> ScoreValue {
const auto &location = unit->location();
if (location.row() < 0) return 0; // unplaced unit
@@ -346,8 +342,7 @@ auto UnitValue(
unit->battalion().type() == net::eagle0::shardok::storage::fb::BattalionTypeId_UNDEAD;
const int coordsIndex = location.row() * map->column_count() + location.column();
const auto *terrain = map->terrain()->Get(coordsIndex);
const auto &terrain = map->terrain()->Get(coordsIndex);
double castleMultiplier = 1.0;
// Only give a multiplier for being in a castle if the castle is useful, and the unit is not
// undead
@@ -357,12 +352,14 @@ auto UnitValue(
kCastleMultiplierBonus * (terrain->modifier().castle().integrity() + 25) / 100.0;
}
double onFireMultiplier = 1.0;
if (terrain->modifier().fire().present()) { onFireMultiplier *= kOnFireMultiplier; }
if (terrain->modifier().fire().present() && (isAttacker || attackerWantsCastles)) {
onFireMultiplier *= kOnFireMultiplier;
}
{
for (const auto adjacentCoords = HexMapUtils::GetAdjacentCoords(map, location);
const auto &c : adjacentCoords) {
if (const auto *adjTerrain = GetTerrain(map, c);
adjTerrain && adjTerrain->modifier().fire().present()) {
if (const auto &adjTerrain = GetTerrain(map, c);
adjTerrain->modifier().fire().present()) {
onFireMultiplier *= kAdjacentFireMultiplier;
}
}
@@ -381,8 +378,8 @@ auto UnitValue(
roundsRemaining,
attackerUnits,
defenderUnits,
meteorRange,
meteorCastVigorCost);
settings.Backing().meteor_range(),
settings.Backing().meteor_cast_vigor_cost());
// scouting values
// attack range
@@ -417,7 +414,7 @@ auto UnitValue(
if (const auto commandingUnitId = unit->commanding_unit_id(); commandingUnitId != -1) {
const Unit *commandingUnit = nullptr;
for (const Unit *attackerUnit : attackerUnits) {
if (attackerUnit && attackerUnit->unit_id() == commandingUnitId) {
if (attackerUnit->unit_id() == commandingUnitId) {
commandingUnit = attackerUnit;
break;
}
@@ -425,7 +422,7 @@ auto UnitValue(
if (commandingUnit == nullptr) {
for (const Unit *defenderUnit : defenderUnits) {
if (defenderUnit && defenderUnit->unit_id() == commandingUnitId) {
if (defenderUnit->unit_id() == commandingUnitId) {
commandingUnit = defenderUnit;
break;
}
@@ -46,8 +46,7 @@ auto UnitValue(
const AttackLocations &locationsThisSideCanAttackFrom,
const CoordsSet &locationsInDangerFromEnemy,
const ActionPointDistances *distances,
int meteorRange,
double meteorCastVigorCost) -> ScoreValue;
const SettingsGetter &settings) -> ScoreValue;
} // namespace shardok
@@ -4,12 +4,10 @@
#include "AIVictoryConditionScoreCalculator.hpp"
#include <algorithm>
#include <ranges>
#include "AIAttackLocations.hpp"
#include "AIDistanceDebuf.hpp"
#include "src/main/cpp/net/eagle0/common/ContainerUtils.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AIAttackGroups.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AIAttackLocations.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AIDistanceDebuf.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/util/HexMapUtils.hpp"
#include "src/main/flatbuffer/net/eagle0/shardok/storage/victory_condition.hpp"
@@ -43,8 +41,8 @@ auto AttackerDebufForOnFireCriticalTile(
const vector<const Unit*>& extinguishingUnits,
const APDCache& apdCache,
const ALCache& alCache,
const BattalionTypeGetter& battalionTypeGetter,
ActionPoints braveWaterCost,
const SettingsGetter& settings,
const int braveWaterActionPointCost,
const bool lateGame) -> double {
double minDebuf = 99999.9;
@@ -60,8 +58,8 @@ auto AttackerDebufForOnFireCriticalTile(
extinguishingUnits,
apdCache,
alCache,
battalionTypeGetter,
braveWaterCost,
settings,
braveWaterActionPointCost,
lateGame,
/* includeUndead = */ false);
if (newDebuf < minDebuf) minDebuf = newDebuf;
@@ -77,8 +75,8 @@ auto AttackerDebufForUnoccupiedCriticalTile(
const vector<const Unit*>& claimableUnits,
const APDCache& apdCache,
const ALCache& alCache,
const BattalionTypeGetter& battalionTypeGetter,
ActionPoints braveWaterCost,
const SettingsGetter& settings,
const int braveWaterActionPointCost,
const bool lateGame) -> double {
return UNHELD_VALUE * DefenderDistanceBuf(
criticalTileLocation,
@@ -87,8 +85,8 @@ auto AttackerDebufForUnoccupiedCriticalTile(
claimableUnits,
apdCache,
alCache,
battalionTypeGetter,
braveWaterCost,
settings,
braveWaterActionPointCost,
lateGame,
/* includeUndead = */ false);
}
@@ -100,8 +98,8 @@ auto AttackerDebufForDefenderOccupiedCriticalTile(
const vector<const Unit*>& attackerUnits,
const APDCache& apdCache,
const ALCache& alCache,
const BattalionTypeGetter& battalionTypeGetter,
ActionPoints braveWaterCost,
const SettingsGetter& settings,
const int braveWaterActionPointCost,
const bool lateGame) {
const double baseUnitValue =
defenderUnit->battalion().size() +
@@ -117,16 +115,19 @@ auto AttackerDebufForDefenderOccupiedCriticalTile(
attackerUnits,
apdCache,
alCache,
battalionTypeGetter,
braveWaterCost,
settings,
braveWaterActionPointCost,
lateGame,
/* includeUndead = */ false);
}
auto DefenderHoldsCriticalTilesVictoryScore(
const GameStateW& gameState,
const net::eagle0::shardok::storage::fb::GameState* gameState,
const CoordsSet& criticalTileLocations,
const PlayerInfo* player) -> ScoreValue {
const PlayerInfo* player,
const APDCache& apdCache,
const ALCache& alCache,
const SettingsGetter& settings) -> ScoreValue {
ScoreValue total = 0.0;
const auto rc = gameState->hex_map()->row_count();
@@ -151,13 +152,12 @@ auto DefenderHoldsCriticalTilesVictoryScore(
}
auto AttackerHoldsCriticalTilesVictoryScore(
const GameStateW& gameState,
const net::eagle0::shardok::storage::fb::GameState* gameState,
const CoordsSet& criticalTileLocations,
const PlayerInfo* player,
const APDCache& apdCache,
const ALCache& alCache,
const BattalionTypeGetter& battalionTypeGetter,
ActionPoints braveWaterCost) -> ScoreValue {
const SettingsGetter& settings) -> ScoreValue {
vector<const Unit*> playerUnits{};
vector<const Unit*> claimablePlayerUnits{};
for (const Unit* unit : *gameState->units()) {
@@ -173,6 +173,7 @@ auto AttackerHoldsCriticalTilesVictoryScore(
return criticalTileLocations.size() * MAX_DEFENDER_HELD_VALUE;
}
const int braveWaterActionPointCost = settings.Backing().brave_water_action_point_cost();
const MapId mapId = apdCache->GetMapId(gameState->hex_map());
ScoreValue total = 0.0;
@@ -199,8 +200,8 @@ auto AttackerHoldsCriticalTilesVictoryScore(
claimablePlayerUnits,
apdCache,
alCache,
battalionTypeGetter,
braveWaterCost,
settings,
braveWaterActionPointCost,
IsLateGame(gameState));
total += BADLY_HELD_VALUE;
}
@@ -212,8 +213,8 @@ auto AttackerHoldsCriticalTilesVictoryScore(
playerUnits,
apdCache,
alCache,
battalionTypeGetter,
braveWaterCost,
settings,
braveWaterActionPointCost,
IsLateGame(gameState));
}
} else if (terrain->modifier().fire().present()) {
@@ -224,8 +225,8 @@ auto AttackerHoldsCriticalTilesVictoryScore(
claimablePlayerUnits,
apdCache,
alCache,
battalionTypeGetter,
braveWaterCost,
settings,
braveWaterActionPointCost,
IsLateGame(gameState));
} else {
total -= AttackerDebufForUnoccupiedCriticalTile(
@@ -235,8 +236,8 @@ auto AttackerHoldsCriticalTilesVictoryScore(
claimablePlayerUnits,
apdCache,
alCache,
battalionTypeGetter,
braveWaterCost,
settings,
braveWaterActionPointCost,
IsLateGame(gameState));
}
}
@@ -245,13 +246,12 @@ auto AttackerHoldsCriticalTilesVictoryScore(
}
auto LastPlayerStandingVictoryScore(
const GameStateW& gameState,
const GameState* gameState,
const PlayerInfo* player,
const APDCache& apdCache,
const ALCache& alCache,
const BattalionTypeGetter& battalionTypeGetter,
ActionPoints braveWaterCost) -> ScoreValue {
if (!std::ranges::contains(
const SettingsGetter& settings) -> ScoreValue {
if (!common::Contains(
*player->victory_conditions(),
net::eagle0::shardok::storage::fb::
VictoryCondition_VICTORY_CONDITION_LAST_PLAYER_STANDING)) {
@@ -283,8 +283,8 @@ auto LastPlayerStandingVictoryScore(
playerUnits,
apdCache,
alCache,
battalionTypeGetter,
braveWaterCost,
settings,
5,
IsLateGame(gameState),
/* includeUndead = */ true);
}
@@ -9,8 +9,6 @@
#include "src/main/cpp/net/eagle0/shardok/ai/AIAttackGroups.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AIAttackLocations.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AICommonTypes.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/GameStateW.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/action_point_distances/ActionPointDistancesCache.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/map/CoordsSet.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/settings/GameSettings.hpp"
@@ -25,26 +23,27 @@ using std::vector;
using ScoreValue = double;
auto AttackerHoldsCriticalTilesVictoryScore(
const GameStateW& gameState,
const net::eagle0::shardok::storage::fb::GameState* gameState,
const CoordsSet& criticalTileLocations,
const PlayerInfo* player,
const APDCache& apdCache,
const ALCache& alCache,
const BattalionTypeGetter& battalionTypeGetter,
ActionPoints braveWaterCost) -> ScoreValue;
const SettingsGetter& settings) -> ScoreValue;
auto DefenderHoldsCriticalTilesVictoryScore(
const GameStateW& gameState,
const net::eagle0::shardok::storage::fb::GameState* gameState,
const CoordsSet& criticalTileLocations,
const PlayerInfo* player) -> ScoreValue;
auto LastPlayerStandingVictoryScore(
const GameStateW& gameState,
const PlayerInfo* player,
const APDCache& apdCache,
const ALCache& alCache,
const BattalionTypeGetter& battalionTypeGetter,
ActionPoints braveWaterCost) -> ScoreValue;
const SettingsGetter& settings) -> ScoreValue;
auto LastPlayerStandingVictoryScore(
const GameState* gameState,
const PlayerInfo* player,
const APDCache& apdCache,
const ALCache& alCache,
const SettingsGetter& settings) -> ScoreValue;
} // namespace shardok
@@ -11,11 +11,11 @@
namespace shardok {
auto UnitIdsRequiringWaterCrossing(
const GameStateW &gameState,
const GameState *gameState,
const PlayerId pid,
const CoordsSet &destinations,
const APDCache &apdCache,
const BattalionTypeGetter &battalionTypeGetter) -> vector<UnitId> {
const SettingsGetter &settings) -> vector<UnitId> {
// Put out all the fires, except on bridges
fb::HexMapW mapCopy = fb::CopyHexMap(gameState->hex_map());
for (uint32_t index = 0; index < mapCopy->terrain()->size(); index++) {
@@ -36,7 +36,7 @@ auto UnitIdsRequiringWaterCrossing(
for (const auto *unit : *gameState->units()) {
if (unit->player_id() != pid) continue;
const auto &battType = battalionTypeGetter(unit->battalion().type());
const auto &battType = settings.GetBattalionType(unit->battalion().type());
if (unit->status() == net::eagle0::shardok::storage::fb::UnitStatus_NORMAL_UNIT) {
for (const Coords &destination : destinations) {
@@ -74,9 +74,10 @@ auto UnitIdsRequiringWaterCrossing(
}
auto UnitIdsToCreateWaterCrossing(
const GameStateW &gameState,
const GameState *gameState,
const PlayerId pid,
const BattalionTypeGetter &battalionTypeGetter) -> vector<UnitId> {
const APDCache &apdCache,
const SettingsGetter &settings) -> vector<UnitId> {
vector<UnitId> unitIds{};
for (const auto *unit : *gameState->units()) {
@@ -87,7 +88,7 @@ auto UnitIdsToCreateWaterCrossing(
if (!unit->has_attached_hero()) continue;
const auto profession = unit->attached_hero().profession_info().profession();
const auto &battalionType = battalionTypeGetter(unit->battalion().type());
const auto &battalionType = settings.GetBattalionType(unit->battalion().type());
if (profession == net::eagle0::shardok::storage::fb::Profession_ENGINEER ||
(profession == net::eagle0::shardok::storage::fb::Profession_MAGE &&
@@ -195,17 +196,17 @@ auto WaterCrossingTiles(
// Returns the set of tiles that the attacker should try to approach in order to bridge/freeze
auto IntendedCrossingStarts(
const GameStateW &gameState,
const GameState *gameState,
const vector<UnitId> &unitIdsCreatingCrossing,
const CoordsSet &tilesToStartCrossingFrom,
const MapId &mapId,
const APDCache &apdCache,
const BattalionTypeGetter &battalionTypeGetter) -> CoordsSet {
const SettingsGetter &settings) -> CoordsSet {
CoordsSet intendedCrossingStarts(gameState->hex_map());
const MapId mapId = apdCache->GetMapId(gameState->hex_map());
for (const UnitId uid : unitIdsCreatingCrossing) {
const Unit *unit = gameState->units()->Get(uid);
const Coords &location = unit->location();
const auto &battalionType = battalionTypeGetter(unit->battalion().type());
const auto &battalionType = settings.GetBattalionType(unit->battalion().type());
const auto *apd = apdCache->GetRaw(gameState->hex_map(), mapId, battalionType, false);
if (location.row() >= 0) {
@@ -218,111 +219,4 @@ auto IntendedCrossingStarts(
return intendedCrossingStarts;
}
using Unit = net::eagle0::shardok::storage::fb::Unit;
constexpr double kNoRequiredCrossingScore = std::numeric_limits<double>::max();
constexpr double kNoCrossingCreatorsScore = std::numeric_limits<double>::min();
auto WaterCrossingScore(
const PlayerId playerId,
const BattalionTypeGetter &battalionTypeGetter,
const GameStateW &gameState,
const CoordsSet &castleCoords,
const CoordsSet &startCrossingFrom,
const APDCache &apdCache) -> double {
uint32_t castleClaimCount = 0;
for (const auto *unit : *gameState->units()) {
if (unit->player_id() != playerId) continue;
const auto status = unit->status();
if (status != net::eagle0::shardok::storage::fb::UnitStatus_NORMAL_UNIT &&
status != net::eagle0::shardok::storage::fb::UnitStatus_RESERVE_UNIT)
continue;
if (!unit->has_attached_hero()) continue;
++castleClaimCount;
}
CoordsSet destinations = castleCoords;
if (castleClaimCount < castleCoords.size()) {
destinations = CoordsSet(gameState->hex_map());
for (const auto *enemyUnit : *gameState->units()) {
if (enemyUnit->player_id() == playerId) continue;
const auto status = enemyUnit->status();
if (status != net::eagle0::shardok::storage::fb::UnitStatus_NORMAL_UNIT) continue;
AssertValid(enemyUnit->location(), gameState->hex_map());
destinations.Add(enemyUnit->location());
}
}
const auto unitIdsRequiringCrossing = UnitIdsRequiringWaterCrossing(
gameState,
playerId,
castleCoords,
apdCache,
battalionTypeGetter);
if (unitIdsRequiringCrossing.empty()) return kNoRequiredCrossingScore;
const auto unitIdsCreatingCrossing =
UnitIdsToCreateWaterCrossing(gameState, playerId, battalionTypeGetter);
if (unitIdsCreatingCrossing.empty()) return kNoCrossingCreatorsScore;
double totalScore = 0;
const auto mapId = ActionPointDistancesCache::GetMapId(gameState->hex_map());
// First put a big penalty on the distance for units that can create a crossing
for (const UnitId uid : unitIdsCreatingCrossing) {
const Unit *unit = gameState->units()->Get(uid);
const auto &battalionType = battalionTypeGetter(unit->battalion().type());
Coords location = unit->location();
int thisDistance;
if (location.row() < 0) thisDistance = 1000;
else {
const auto *apd = apdCache->GetRaw(gameState->hex_map(), mapId, battalionType, false);
thisDistance = MinimumDistance(apd, location, startCrossingFrom);
}
totalScore -= thisDistance * 100.0;
}
// Now a smaller penalty for distance for units that need to cross, except if they block -- then
// a large penalty
for (const UnitId uid : unitIdsRequiringCrossing) {
// If this unit ID can also create a crossing, we already handled it
if (std::ranges::contains(unitIdsCreatingCrossing, uid)) continue;
const Unit *unit = gameState->units()->Get(uid);
const auto &battalionType = battalionTypeGetter(unit->battalion().type());
Coords location = unit->location();
const auto *apd = apdCache->GetRaw(gameState->hex_map(), mapId, battalionType, false);
int thisDistance;
if (location.row() < 0) thisDistance = 1000;
else { thisDistance = MinimumDistance(apd, location, startCrossingFrom); }
bool targetBlocks = false;
// If we're not capable of creating a crossing, don't get in the way of somebody that is.
for (const UnitId crossingUid : unitIdsCreatingCrossing) {
const auto *crossingCapableUnit = gameState->units()->Get(crossingUid);
// Don't check for units that aren't yet placed
if (crossingCapableUnit->location().row() < 0) continue;
AssertValid(crossingCapableUnit->location(), gameState->hex_map());
if (thisDistance <
MinimumDistance(apd, crossingCapableUnit->location(), startCrossingFrom)) {
targetBlocks = true;
break;
}
}
if (targetBlocks) continue;
totalScore -= thisDistance;
}
return totalScore;
}
} // namespace shardok
@@ -5,8 +5,6 @@
#ifndef EAGLE0_AIWATERCROSSINGCALCULATOR_HPP
#define EAGLE0_AIWATERCROSSINGCALCULATOR_HPP
#include "src/main/cpp/net/eagle0/shardok/ai/AICommonTypes.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/GameStateW.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/action_point_distances/ActionPointDistancesCache.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/map/CoordsSet.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/settings/GameSettings.hpp"
@@ -31,17 +29,18 @@ static inline void AssertValid(const Coords& c, const HexMap* hexMap) {
// Units that need a water crossing to reach at least one of the destinations
auto UnitIdsRequiringWaterCrossing(
const GameStateW& gameState,
const GameState* gameState,
PlayerId pid,
const CoordsSet& destinations,
const APDCache& apdCache,
const BattalionTypeGetter& battalionTypeGetter) -> vector<UnitId>;
const SettingsGetter& settings) -> vector<UnitId>;
// Units belonging to the player that are capable of creating water crossings
auto UnitIdsToCreateWaterCrossing(
const GameStateW& gameState,
const GameState* gameState,
PlayerId pid,
const BattalionTypeGetter& battalionTypeGetter) -> vector<UnitId>;
const APDCache& apdCache,
const SettingsGetter& settings) -> vector<UnitId>;
// Whether a unit of the given type can reach destination from origin, given the current state
// of the map
@@ -68,20 +67,12 @@ auto WaterCrossingTiles(
// Returns the set of tiles that the attacker should try to approach in order to bridge/freeze
auto IntendedCrossingStarts(
const GameStateW& gameState,
const GameState* gameState,
const vector<UnitId>& unitIdsCreatingCrossing,
const CoordsSet& tilesToStartCrossingFrom,
const MapId& mapId,
const APDCache& apdCache,
const BattalionTypeGetter& battalionTypeGetter) -> CoordsSet;
// Calculate score based on water crossing strategy
auto WaterCrossingScore(
PlayerId playerId,
const BattalionTypeGetter& battalionTypeGetter,
const GameStateW& gameState,
const CoordsSet& castleCoords,
const CoordsSet& startCrossingFrom,
const APDCache& apdCache) -> double;
const SettingsGetter& settings) -> CoordsSet;
} // namespace shardok
@@ -4,10 +4,8 @@
#include "AIWaterCrossingCommandChooser.hpp"
#include <algorithm>
#include <ranges>
#include "AIMinimumDistanceAndTarget.hpp"
#include "src/main/cpp/net/eagle0/common/ContainerUtils.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AIWaterCrossingCalculator.hpp"
namespace shardok {
@@ -18,11 +16,11 @@ constexpr ScoreValue kNoRequiredCrossingScore = std::numeric_limits<ScoreValue>:
constexpr ScoreValue kNoCrossingCreatorsScore = std::numeric_limits<ScoreValue>::min();
[[nodiscard]] auto AIWaterCrossingCommandChooser::WaterCrossingScore(
const BattalionTypeGetter &battalionTypeGetter,
const GameStateW &gameState,
const SettingsGetter &settingsGetter,
const GameState *gameState,
const CoordsSet &castleCoords,
const CoordsSet &startCrossingFrom) const -> ScoreValue {
uint32_t castleClaimCount = 0;
int castleClaimCount = 0;
for (const auto *unit : *gameState->units()) {
if (unit->player_id() != playerId) continue;
const auto status = unit->status();
@@ -51,13 +49,15 @@ constexpr ScoreValue kNoCrossingCreatorsScore = std::numeric_limits<ScoreValue>:
playerId,
castleCoords,
apdCache,
battalionTypeGetter);
settingsGetter);
if (unitIdsRequiringCrossing.empty()) return kNoRequiredCrossingScore;
const auto unitIdsCreatingCrossing =
UnitIdsToCreateWaterCrossing(gameState, playerId, battalionTypeGetter);
UnitIdsToCreateWaterCrossing(gameState, playerId, apdCache, settingsGetter);
if (unitIdsCreatingCrossing.empty()) return kNoCrossingCreatorsScore;
fprintf(stderr, "%lu units require a water crossing\n", unitIdsRequiringCrossing.size());
ScoreValue totalScore = 0;
const auto mapId = ActionPointDistancesCache::GetMapId(gameState->hex_map());
@@ -65,7 +65,7 @@ constexpr ScoreValue kNoCrossingCreatorsScore = std::numeric_limits<ScoreValue>:
// First put a big penalty on the distance for units that can create a crossing
for (const UnitId uid : unitIdsCreatingCrossing) {
const Unit *unit = gameState->units()->Get(uid);
const auto &battalionType = battalionTypeGetter(unit->battalion().type());
const auto &battalionType = settingsGetter.GetBattalionType(unit->battalion().type());
Coords location = unit->location();
int thisDistance;
@@ -83,10 +83,10 @@ constexpr ScoreValue kNoCrossingCreatorsScore = std::numeric_limits<ScoreValue>:
// a large penalty
for (const UnitId uid : unitIdsRequiringCrossing) {
// If this unit ID can also create a crossing, we already handled it
if (std::ranges::contains(unitIdsCreatingCrossing, uid)) continue;
if (common::Contains(unitIdsCreatingCrossing, uid)) continue;
const Unit *unit = gameState->units()->Get(uid);
const auto &battalionType = battalionTypeGetter(unit->battalion().type());
const auto &battalionType = settingsGetter.GetBattalionType(unit->battalion().type());
Coords location = unit->location();
const auto *apd = apdCache->GetRaw(gameState->hex_map(), mapId, battalionType, false);
@@ -118,12 +118,12 @@ constexpr ScoreValue kNoCrossingCreatorsScore = std::numeric_limits<ScoreValue>:
}
auto AIWaterCrossingCommandChooser::StartCrossingFrom(
const BattalionTypeGetter &battalionTypeGetter,
const GameStateW &gameState,
const SettingsGetter &settingsGetter,
const GameState *gameState,
const CoordsSet &castleCoords) const -> CoordsSet {
CoordsSet startCrossingFrom(gameState->hex_map());
uint32_t castleClaimCount = 0;
int castleClaimCount = 0;
for (const auto *unit : *gameState->units()) {
if (unit->player_id() != playerId) continue;
const auto status = unit->status();
@@ -152,16 +152,16 @@ auto AIWaterCrossingCommandChooser::StartCrossingFrom(
playerId,
castleCoords,
apdCache,
battalionTypeGetter);
settingsGetter);
if (unitIdsRequiringCrossing.empty()) return startCrossingFrom;
const auto unitIdsCreatingCrossing =
UnitIdsToCreateWaterCrossing(gameState, playerId, battalionTypeGetter);
UnitIdsToCreateWaterCrossing(gameState, playerId, apdCache, settingsGetter);
if (unitIdsCreatingCrossing.empty()) return startCrossingFrom;
for (const UnitId uid : unitIdsRequiringCrossing) {
const Unit *unit = gameState->units()->Get(uid);
const auto &battalionType = battalionTypeGetter(unit->battalion().type());
const auto &battalionType = settingsGetter.GetBattalionType(unit->battalion().type());
Coords origin = unit->location();
// FIXME: this is just grabbing the first starting position, ideally we'd try them all
@@ -6,16 +6,18 @@
#define EAGLE0_AIWATERCROSSINGCOMMANDCHOOSER_HPP
#include <utility>
#include <vector>
#include "src/main/cpp/net/eagle0/shardok/ai/AICommonTypes.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/GameStateW.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/action_point_distances/ActionPointDistancesCache.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/fb_helpers/FlatbufferWrapper.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/map/CoordsSet.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/settings/GameSettings.hpp"
#include "src/main/flatbuffer/net/eagle0/shardok/storage/game_state.hpp"
#include "src/main/protobuf/net/eagle0/shardok/api/command_descriptor.pb.h"
namespace shardok {
using CommandProto = net::eagle0::shardok::api::CommandDescriptor;
using GameState = net::eagle0::shardok::storage::fb::GameState;
using Unit = net::eagle0::shardok::storage::fb::Unit;
using ScoreValue = double;
@@ -30,14 +32,14 @@ public:
: playerId(pid),
apdCache(std::move(apdCache)) {}
[[nodiscard]] auto StartCrossingFrom(
const BattalionTypeGetter &battalionTypeGetter,
const GameStateW &gameState,
auto StartCrossingFrom(
const SettingsGetter &settingsGetter,
const GameState *gameState,
const CoordsSet &castleCoords) const -> CoordsSet;
[[nodiscard]] auto WaterCrossingScore(
const BattalionTypeGetter &battalionTypeGetter,
const GameStateW &gameState,
const SettingsGetter &settingsGetter,
const GameState *gameState,
const CoordsSet &castleCoords,
const CoordsSet &startCrossingFrom) const -> ScoreValue;
};
@@ -210,556 +210,4 @@ Where:
- **Magnitude**: Indicates confidence/importance of the evaluation
- **Relative scoring**: Only score differences matter, not absolute values
This scoring system provides a robust framework for tactical AI decision-making, balancing immediate tactical gains with strategic objectives while handling the uncertainty inherent in combat outcomes.
## AIScoreCalculator Function Reference
### Public Interface Functions
#### `GuessedStateScore`
**Purpose**: Evaluates the score of a game state from the perspective of the current AI strategy without performing any commands.
**Parameters**:
- `isDefender`: Whether the AI is playing as defender
- `state`: Current game state to evaluate
- `aiStrategy`: Strategy being used (attack castles, hold castles, scatter, etc.)
- `allCastleCoords`: Set of all castle coordinates on the map
- `settingsGetter`: Game configuration and rules
- `apdCache`: Cached action point distances for movement calculations
- `alCache`: Cached attack locations for combat calculations
**Returns**: Score value representing how favorable the state is for the evaluating player (positive = good, negative = bad)
#### `CommandScore`
**Purpose**: Evaluates the score for a specific command using lookahead search to consider future consequences.
**Parameters**:
- `pid`: Player ID executing the command
- `isDefender`: Whether the player is a defender
- `remainingLookahead`: Depth of recursive search remaining
- `maxRepeatCount`: Number of random simulations for non-deterministic commands
- `guessedEngine`: Current game engine state
- `attackerStrategy`: Strategy being used by attackers
- `currentUtility`: Current game state score before command execution
- `settingsGetter`: Game configuration
- `allCastleCoords`: Castle locations
- `apdCache` & `alCache`: Cached distance/attack calculations
- `commandIndex`: Index of command to evaluate
- `deadline`: Time limit for computation
**Returns**: Future containing the final score after lookahead evaluation
### Internal Core Functions
#### `BuildDecisionTree` (NEW)
**Purpose**: Builds a complete decision tree containing all evaluated command paths up to the specified depth.
**Process**:
1. Filters commands using `AICommandFilter` to reduce search space
2. For each command, calls `ExecuteCommandForTree` to build complete subtrees
3. Returns full tree with all possible moves and their consequences
4. Identifies best command within the complete tree structure
**Returns**: `std::future<CommandDecisionTree>` containing the complete decision tree
#### `BestCommandIndex` (Legacy - Wrapper)
**Purpose**: Backward compatibility wrapper that uses `BuildDecisionTree` but returns traditional `IndexAndScore`.
**Process**:
1. Calls `BuildDecisionTree` to get complete tree
2. Extracts best command information for compatibility
3. Returns only the optimal command details in legacy format
#### `ExecuteCommandForTree` (NEW)
**Purpose**: Executes a command and creates a tree node with the resulting game state and scores.
**Process**:
1. Creates engine copy and executes the command with given random seed
2. Creates `CommandTreeNode` with command results and game state
3. Calculates immediate score using `GuessedStateScore`
4. Calls `RecursiveTreeBuilder` to populate child nodes if depth allows
5. Calculates lookahead score from children (or uses immediate score)
**Returns**: `std::unique_ptr<CommandTreeNode>` containing the command execution results and subtree
#### `RecursiveTreeBuilder` (NEW)
**Purpose**: Recursively populates child nodes of a tree node by building subtrees for subsequent moves.
**Process**:
1. Gets available commands for the next player
2. Filters commands to reduce search space
3. For each command, calls `ExecuteCommandForTree` to create child nodes
4. Handles different command types (deterministic, odds-based, random)
5. Populates the parent node's children vector with complete subtrees
#### `CalcOne` (Legacy)
**Purpose**: Executes a single command simulation with specified randomness and returns both immediate and lookahead scores.
**Process**:
1. Creates engine copy and executes the command with given random seed
2. Calculates immediate score using `GuessedStateScore`
3. Initiates recursive lookahead calculation if depth remains
4. Handles timeouts gracefully by returning default scores
#### `EvaluateCommand`
**Purpose**: Lower-level command evaluation that handles different command types appropriately.
**Command Type Handling**:
- **Deterministic**: Single evaluation with average randomness (0.5)
- **Odds-based**: Two evaluations (success/failure) weighted by success probability
- **Non-deterministic**: Multiple evaluations with distributed random values, averaged
#### `BasicLookaheadCalculator`
**Purpose**: Recursive lookahead search that finds the best future command sequence and propagates scores backward.
**Features**:
- Uses transposition table to cache previously computed positions
- Handles depth limits and terminal states
- Returns futures for asynchronous computation
- Stores results in transposition table for reuse
### Strategy-Specific Scoring Functions
#### `AttackerScoreForState`
**Purpose**: Calculates state score from attacker perspective based on strategy type.
**Strategy Support**:
- `STRATEGY_ATTACK_CASTLES`: Prioritizes capturing castle positions
- `STRATEGY_ATTACK_UNITS`: Focuses on eliminating defender units
- `STRATEGY_HOLD_CASTLES`: Maintains control of captured castles
- `STRATEGY_CROSS_RIVERS`: Special water crossing objectives
- `STRATEGY_FLEE`: Escape-focused scoring
#### `DefenderScoreForState`
**Purpose**: Calculates state score from defender perspective.
**Strategy Support**:
- `STRATEGY_HOLD_CASTLES`: Defend critical castle positions
- `STRATEGY_SCATTER`: Spread units to avoid elimination
- `STRATEGY_FLEE`: Escape-focused scoring
#### `AttackerUnitsScore`
**Purpose**: Core unit valuation function that calculates total value of all units on the board with contextual modifiers.
**Features**:
- Uses `UnitValue` for individual unit calculations
- Applies distance multipliers based on proximity to objectives
- Handles special cases like undead, VIP units, and scattered defenders
- Incorporates castle bonuses and environmental penalties
### Specialized Strategy Functions
#### `DefenderScatterStrategyScoreForState`
**Purpose**: Implements scatter strategy scoring that rewards defensive units for staying far from enemies and friendlies.
#### `DefenderHoldCastlesStrategyScoreForState`
**Purpose**: Implements castle defense strategy with victory condition scoring.
#### `FleeStrategyScoreForState`
**Purpose**: Implements flee strategy that heavily penalizes remaining on the battlefield.
### Utility Functions
#### `AttackerMultiplierForTargetDistance`
**Purpose**: Calculates distance-based scoring multipliers for attackers based on proximity to priority targets.
**Features**:
- Uses recursive priority list evaluation
- Accounts for occupied vs. unoccupied targets
- Incorporates brave water crossing capabilities
- Uses cached action point distances for efficiency
#### `CommandSorter`
**Purpose**: Comparison function for ranking commands by lookahead score (primary) and immediate score (tiebreaker).
#### `IsDeterministic`
**Purpose**: Determines if a command type has predictable outcomes or requires random simulation.
### Performance and Caching
#### `EffectiveDistanceCache`
**Purpose**: Memoization cache for expensive distance calculations between units and targets.
#### `AttackerScorePerformanceLogger`
**Purpose**: Performance monitoring system that tracks call frequency and timing for `AttackerScoreForState`.
The function architecture supports parallel evaluation, caching, and recursive lookahead while maintaining separation between strategy-specific logic and core evaluation mechanics.
## Decision Tree Data Structures (NEW)
### CommandTreeNode
**Purpose**: Represents a single command execution and its consequences in the decision tree.
**Key Fields**:
- `commandIndex`: Index of the command in the original command list
- `commandType`: Type of command (MOVE, MELEE, END_TURN, etc.)
- `immediateScore`: Score of the game state immediately after this command
- `lookaheadScore`: Best achievable score considering future moves
- `resultingGameState`: Game state after command execution
- `children`: Vector of child nodes representing subsequent possible moves
- `playerId`, `depth`, `isDefender`: Metadata about the command context
**Features**:
- Stores complete game state for each decision point
- Maintains parent-child relationships for tree traversal
- Supports both immediate and lookahead scoring
- Contains metadata for debugging and analysis
### CommandDecisionTree
**Purpose**: Complete decision tree containing all evaluated command paths from a given position.
**Key Fields**:
- `rootNodes`: All possible first moves from the starting position
- `bestCommand`: Pointer to the optimal root command
- `maxDepth`: Maximum lookahead depth of the tree
- `totalNodes`: Total number of nodes in the tree (for statistics)
**Features**:
- Provides complete visibility into AI decision-making process
- Enables analysis of alternative moves and their consequences
- Supports tree statistics and debugging information
- Maintains backward compatibility through `GetBestCommandIndex()`
**Memory Management**:
- Uses `std::unique_ptr` for automatic memory cleanup
- `GameStateW` objects are stored directly (not shared pointers for simplicity)
- Tree structure ensures proper cleanup when nodes go out of scope
### Tree vs. Legacy Approach Comparison
| Aspect | Legacy (Single Best) | Tree-Based (Complete) |
|--------|---------------------|----------------------|
| **Output** | Best command only | Complete decision tree |
| **Memory** | Minimal | Higher (stores all paths) |
| **Analysis** | Limited visibility | Full decision transparency |
| **Debugging** | Single command info | Complete move sequences |
| **Performance** | Slightly faster | Comparable (same calculations) |
| **Compatibility** | Direct usage | Wrapper maintains compatibility |
### Usage Patterns
**For AI Decision Making**:
```cpp
auto treeFuture = BuildDecisionTree(pid, isDefender, depth, maxRepeat,
engine, strategy, utility, settings,
castles, apdCache, alCache, deadline);
CommandDecisionTree tree = treeFuture.get();
size_t bestCommand = tree.bestCommand->commandIndex;
```
**For Analysis and Debugging**:
```cpp
CommandDecisionTree tree = treeFuture.get();
// Examine all possible moves
for (const auto& rootNode : tree.rootNodes) {
std::cout << "Command " << rootNode->commandIndex
<< " Score: " << rootNode->lookaheadScore << std::endl;
// Traverse children to see consequences
for (const auto& child : rootNode->children) {
// ... analyze child moves
}
}
```
**Legacy Compatibility**:
```cpp
// Existing code continues to work unchanged
auto indexScoreFuture = BestCommandIndex(pid, isDefender, ...);
IndexAndScore result = indexScoreFuture.get();
size_t bestCommand = result.index;
```
The tree-based approach provides complete decision transparency while maintaining full backward compatibility with existing AI code.
## MCTS Alternative: Randomness Handling Recommendations
The new MCTS-based AI system is available in `MCTSAI.hpp/.cpp` and provides an alternative to the iterative deepening approach. However, the current MCTS implementation uses simplified randomness handling compared to the sophisticated approach in the original system.
### Current MCTS Limitations
1. **Expansion Phase**: Uses average rolls (0.5) for all commands during tree expansion
2. **Simulation Phase**: Uses random command selection with average rolls
3. **Missing**: No explicit chance nodes for commands with `HasOdds()`
4. **Missing**: No multi-sample evaluation for stochastic commands
### Recommended Improvements: Chance Node Integration
#### 1. **Explicit Chance Nodes** (Highest Priority)
For commands with `HasOdds()`, create explicit chance nodes in the MCTS tree:
```cpp
// During MCTSExpansion
if (descriptor->HasOdds()) {
// Create TWO child nodes: success and failure
auto successNode = CreateMCTSNode(commandIndex, SUCCESS_VARIANT);
auto failureNode = CreateMCTSNode(commandIndex, FAILURE_VARIANT);
// Execute with deterministic rolls (matching original system)
ExecuteWithRoll(successNode, 1.0 - successChance/2.0); // High roll
ExecuteWithRoll(failureNode, (1.0 - successChance)/2.0); // Low roll
// Set probability weights for selection
successNode->probabilityWeight = successChance;
failureNode->probabilityWeight = 1.0 - successChance;
}
```
#### 2. **Weighted Selection for Chance Nodes**
Modify `MCTSSelection` to handle chance nodes:
```cpp
if (node->isChanceNode) {
// Select based on probability distribution, not UCB1
return SelectByProbability(node->children);
} else {
// Normal UCB1 selection for decision nodes
return node->GetBestChild(explorationConstant);
}
```
#### 3. **Probability-Weighted Backpropagation**
Update backpropagation to account for chance node probabilities:
```cpp
void MCTSBackpropagation(MCTSNode* node, double reward) {
while (node) {
node->visitCount++;
// Weight reward by probability for chance nodes
double weightedReward = reward;
if (node->parent && node->parent->isChanceNode) {
weightedReward *= node->probabilityWeight;
}
node->totalReward += weightedReward;
node->averageReward = node->totalReward / node->visitCount;
node = node->parent;
}
}
```
#### 4. **Multi-Sample Commands**
For commands without explicit odds but with randomness, use stratified sampling:
```cpp
// During expansion, create multiple child nodes with different rolls
for (int sample = 0; sample < numSamples; ++sample) {
double roll = static_cast<double>(sample) / (numSamples - 1);
auto sampleNode = CreateMCTSNodeWithRoll(commandIndex, roll);
sampleNode->probabilityWeight = 1.0 / numSamples;
}
```
### Benefits of Chance Node Integration
1. **Accurate Evaluation**: Preserves the sophisticated randomness handling from the original system
2. **Better Convergence**: MCTS can properly explore both success/failure outcomes
3. **Realistic Simulations**: Tree accurately represents game's probability distributions
4. **Comparable Results**: Makes MCTS results directly comparable to iterative deepening
### Implementation Priority
1. **Phase 1**: Add explicit chance nodes for `HasOdds()` commands
2. **Phase 2**: Implement probability-weighted selection and backpropagation
3. **Phase 3**: Add multi-sample support for general stochastic commands
4. **Phase 4**: Optimize performance with lazy expansion of chance nodes
### Alternative: Determinization Approach
If explicit chance nodes prove too complex, consider **determinization**:
- Run multiple MCTS trees with different fixed random seeds
- Aggregate results across all determinizations
- Simpler to implement but potentially less accurate than explicit chance nodes
### Switching Between AI Systems
Both AI systems (`IterativeDeepeningAI` and `MCTSAI`) implement compatible interfaces. The algorithm is selected at **runtime** via the ShardokAIClient constructor:
```cpp
// Using Iterative Deepening (default)
ShardokAIClient client(playerId, isDefender, hexMap, settings);
// Or explicitly:
ShardokAIClient client(playerId, isDefender, hexMap, settings,
AIAlgorithmType::ITERATIVE_DEEPENING);
// Using MCTS
ShardokAIClient client(playerId, isDefender, hexMap, settings,
AIAlgorithmType::MCTS);
// Note: MCTS configuration can be customized via MCTSConfig:
// - maxIterations: 10000 (max MCTS iterations per move)
// - maxSimulationDepth: 10 (depth for rollout phase)
// - maxTreeDepth: 20 (max tree depth to prevent stack overflow)
// - explorationConstant: 1.414 (UCB1 exploration vs exploitation)
// - useMultithreading: true (APD cache is thread-safe with TLS + mutex protection)
// - numThreads: 4
```
The selection is made per AI client instance, allowing different algorithms for different players or game situations within the same server process.
#### Direct AI Usage (Lower Level)
Both AI systems can also be used directly:
```cpp
// Using Iterative Deepening directly
auto iterativeAI = IterativeDeepeningAI(playerId, isDefender, strategy,
castleCoords, apdCache, alCache);
auto result = iterativeAI.IterativeSearch(settings, state, commands, budget);
// Using MCTS directly
auto mctsAI = MCTSAI(playerId, isDefender, strategy,
castleCoords, apdCache, alCache);
auto result = mctsAI.Search(settings, state, commands, budget);
```
#### Algorithm Comparison
| Feature | Iterative Deepening | MCTS |
|---------|-------------------|------|
| **Randomness Handling** | Sophisticated (chance nodes, multi-sample) | Simplified (average rolls) |
| **Performance** | Single-threaded | Multithreaded |
| **Search Type** | Fixed depth with iterative deepening | Adaptive with time budget |
| **Memory Usage** | Lower | Higher (maintains tree) |
| **Max Tree Depth** | Limited by lookahead setting | Limited by `maxTreeDepth` config (default: 20) |
| **Tree Destruction** | Not applicable | Iterative (avoids stack overflow) |
| **Best For** | Precise evaluation, production | Performance testing, fast decisions |
The MCTS implementation provides a solid foundation. Known limitations:
1. **Randomness Handling**: Simplified compared to iterative deepening (no explicit chance nodes)
2. **Simulation Quality**: Uses random rollouts instead of sophisticated evaluation
Note: The APD cache is fully thread-safe using thread-local storage and mutex-protected shared cache.
Adding chance node handling and ensuring thread safety would make it a superior replacement for the iterative deepening approach while maintaining the sophisticated randomness evaluation that makes the current system effective.
## MCTS Configuration Options
The MCTS AI system provides extensive configuration through the `MCTSConfig` structure:
### Core MCTS Parameters
```cpp
struct MCTSConfig {
double explorationConstant = 1.414; // UCB1 constant (sqrt(2) by default)
int maxSimulationDepth = 1000; // Maximum depth for rollout
int maxTreeDepth = 2000; // Maximum tree depth to prevent stack overflow
bool useMultithreading = true; // Enable parallel MCTS
int numThreads = 16; // Number of threads for parallel MCTS (when enabled)
MCTSSimulationPolicy simulationPolicy = MCTSSimulationPolicy::BEST_IMMEDIATE;
bool enableTranspositionDetection = true; // Enable pruning of duplicate states
double immediateScoreTieBreakThreshold = 5.0; // When avg rewards differ by less than this, prefer higher immediate score
double visitCountTolerance = 0.05; // Treat visit counts as equal if within this % of best count
bool enableImmediateScoreInUCB1 = true; // Apply immediate score tie-breaking in UCB1 selection too
};
```
### Exploration vs Exploitation
- **`explorationConstant`**: Controls the exploration vs exploitation balance in UCB1 selection
- Higher values (>1.414): More exploration of unvisited nodes
- Lower values (<1.414): More exploitation of known good moves
- Default: 1.414 (√2, theoretical optimum for UCB1)
### Tree Structure Limits
- **`maxTreeDepth`**: Prevents stack overflow in deep game trees
- Default: 2000 (very high limit for most tactical scenarios)
- Terminal detection stops expansion when this depth is reached
- **`maxSimulationDepth`**: Controls rollout length during simulation phase
- Default: 1000 (sufficient for most tactical scenarios)
- Longer simulations provide more accurate estimates but use more time
### Multithreading Configuration
- **`useMultithreading`**: Enable/disable parallel MCTS execution
- Default: true (takes advantage of modern multi-core CPUs)
- Requires thread-safe game engine and scoring components
- **`numThreads`**: Number of worker threads for parallel tree building
- Default: 16 (adjust based on available CPU cores)
- More threads can improve search speed but with diminishing returns
### Simulation Policies
The `MCTSSimulationPolicy` enum controls how commands are selected during the rollout phase:
- **`RANDOM`**: Pure random selection from all available commands
- Fastest but least informed simulations
- Good baseline for testing MCTS convergence
- **`FILTERED_RANDOM`**: Random selection from AICommandFilter-approved commands
- Eliminates obviously bad moves (moving away from objectives, etc.)
- Better simulation quality with minimal overhead
- **`BEST_IMMEDIATE`**: Always choose command with highest immediate score
- Most informed simulations
- Slower but higher quality rollouts
- Default setting for production use
- **`WEIGHTED_BEST_IMMEDIATE`**: Random selection weighted by immediate score ranking
- Balances exploration with informed choice
- Alternative to pure greedy selection
### Transposition Detection
- **`enableTranspositionDetection`**: Enable pruning of duplicate game states
- Default: true (improves search efficiency)
- Uses hash-based state identification
- Prevents wasted computation on equivalent positions reached via different move sequences
### Immediate Score Tie-Breaking
These settings address MCTS's tendency to choose indirect paths when direct paths lead to the same outcome:
- **`immediateScoreTieBreakThreshold`**: Score difference threshold for tie-breaking
- Default: 5.0 (when backpropagated rewards differ by less than this, prefer immediate score)
- Helps AI choose direct moves over equivalent indirect sequences
- Improves user experience by reducing unnecessary intermediate moves
- **`visitCountTolerance`**: Visit count equality threshold for tie-breaking
- Default: 0.05 (5% tolerance - visit counts within this percentage are considered equal)
- Prevents minor visit count differences from overriding immediate score preferences
- **`enableImmediateScoreInUCB1`**: Apply immediate score tie-breaking during exploration
- Default: true (consistent tie-breaking in both exploration and final selection)
- When UCB1 values are very close, prefer nodes with higher immediate scores
- Improves convergence on direct paths to objectives
### Usage Example
```cpp
// Custom MCTS configuration for performance testing
MCTSConfig config;
config.explorationConstant = 2.0; // More exploration
config.simulationPolicy = MCTSSimulationPolicy::FILTERED_RANDOM; // Faster rollouts
config.numThreads = 8; // Reduce threads for testing environment
config.immediateScoreTieBreakThreshold = 10.0; // More aggressive tie-breaking
MCTSAI ai(playerId, isDefender, strategy, castleCoords, apdCache, alCache, config);
```
### Configuration Recommendations
**For Production Use:**
- Use default settings for balanced performance and quality
- Consider reducing `numThreads` on systems with limited CPU cores
- `BEST_IMMEDIATE` simulation policy provides highest quality decisions
**For Performance Testing:**
- `FILTERED_RANDOM` or `RANDOM` simulation policies for faster rollouts
- Lower `explorationConstant` (1.0) for more exploitation
- Disable transposition detection for baseline comparison
**For Analysis/Debugging:**
- Single-threaded execution (`useMultithreading = false`) for deterministic results
- Higher `immediateScoreTieBreakThreshold` to emphasize direct paths
- `BEST_IMMEDIATE` simulation for most predictable behavior
The configuration system allows fine-tuning MCTS behavior for different scenarios while maintaining compatibility with the existing AI infrastructure.
This scoring system provides a robust framework for tactical AI decision-making, balancing immediate tactical gains with strategic objectives while handling the uncertainty inherent in combat outcomes.
+48 -112
View File
@@ -1,16 +1,5 @@
load("//tools:copts.bzl", "COPTS")
cc_library(
name = "ai_common_types",
hdrs = ["AICommonTypes.hpp"],
copts = COPTS,
visibility = ["//visibility:public"],
deps = [
"//src/main/cpp/net/eagle0/shardok/library:battalion_type",
"//src/main/cpp/net/eagle0/shardok/library:shardok_c_types",
],
)
cc_library(
name = "ai_attacker_strategy_selector",
srcs = ["AIAttackerStrategySelector.cpp"],
@@ -22,7 +11,6 @@ cc_library(
],
deps = [
":ai_attack_locations",
":ai_flee_decision_calculator",
":ai_score_utilities",
":ai_strategy",
":ai_water_crossing_command_chooser",
@@ -39,15 +27,14 @@ cc_library(
hdrs = ["AIAttackGroups.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai:__subpackages__",
"//src/main/cpp/net/eagle0/shardok/ai_performance_runner:__pkg__",
"//src/test/cpp/net/eagle0/shardok/ai:__subpackages__",
"//src/test/cpp/net/eagle0/shardok/ai:__pkg__",
],
deps = [
":ai_attack_locations",
":ai_common_types",
":ai_score_utilities",
"//src/main/cpp/net/eagle0/shardok/library/action_point_distances:action_point_distances_cache",
"//src/main/cpp/net/eagle0/shardok/library/settings:game_settings",
"//src/main/cpp/net/eagle0/shardok/library/util:hex_map_utils",
"//src/main/flatbuffer/net/eagle0/shardok/storage:hex_map_cc_fbs",
"//src/main/flatbuffer/net/eagle0/shardok/storage:unit_cc_fbs",
@@ -59,10 +46,6 @@ cc_library(
srcs = ["AIAttackLocations.cpp"],
hdrs = ["AIAttackLocations.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai:__subpackages__",
"//src/test/cpp/net/eagle0/shardok/ai:__subpackages__",
],
deps = [
":ai_score_utilities",
"//src/main/cpp/net/eagle0/shardok/library/map:terrain",
@@ -87,7 +70,6 @@ cc_library(
":ai_score_utilities",
":ai_strategy",
":ai_water_crossing_calculator",
"//src/main/cpp/net/eagle0/shardok/library:game_state_w",
"//src/main/cpp/net/eagle0/shardok/library/map:coords_set",
"//src/main/cpp/net/eagle0/shardok/library/util:hex_map_utils",
"//src/main/flatbuffer/net/eagle0/shardok/storage:game_state_cc_fbs",
@@ -101,14 +83,11 @@ cc_library(
hdrs = ["AIDistanceDebuf.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai:__subpackages__",
"//src/main/cpp/net/eagle0/shardok/ai_performance_runner:__pkg__",
"//src/test/cpp/net/eagle0/shardok/ai:__pkg__",
"//src/test/cpp/net/eagle0/shardok/ai:__subpackages__",
],
deps = [
":ai_attack_locations",
":ai_common_types",
":ai_score_utilities",
"//src/main/cpp/net/eagle0/shardok/library/action_point_distances",
"//src/main/cpp/net/eagle0/shardok/library/action_point_distances:action_point_distances_cache",
@@ -137,110 +116,53 @@ cc_library(
hdrs = ["AIScoreUtilities.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai:__subpackages__",
"//src/main/cpp/net/eagle0/shardok/ai_performance_runner:__pkg__",
"//src/test/cpp/net/eagle0/shardok/ai:__pkg__",
"//src/test/cpp/net/eagle0/shardok/ai:__subpackages__",
],
deps = [
"//src/main/cpp/net/eagle0/shardok/library:game_state_w",
"//src/main/cpp/net/eagle0/shardok/library:shardok_c_types",
"//src/main/flatbuffer/net/eagle0/shardok/storage:game_state_cc_fbs",
"//src/main/flatbuffer/net/eagle0/shardok/storage:unit_cc_fbs",
],
)
cc_library(
name = "ai_flee_decision_calculator",
srcs = ["AIFleeDecisionCalculator.cpp"],
hdrs = ["AIFleeDecisionCalculator.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai_performance_runner:__pkg__",
"//src/test/cpp/net/eagle0/shardok/ai:__pkg__",
],
deps = [
":ai_score_utilities",
":ai_unit_score_calculator",
"//src/main/cpp/net/eagle0/shardok/library:engine",
"//src/main/cpp/net/eagle0/shardok/library/util:hex_map_utils",
],
)
cc_library(
name = "ai_heuristic_weighting",
srcs = ["AIHeuristicWeighting.cpp"],
hdrs = ["AIHeuristicWeighting.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai/mcts/adapters:__pkg__",
"//src/test/cpp/net/eagle0/shardok/ai:__pkg__",
],
deps = [
"//src/main/cpp/net/eagle0/shardok/library:game_state_w",
"//src/main/cpp/net/eagle0/shardok/library/action_point_distances:action_point_distances_cache",
"//src/main/cpp/net/eagle0/shardok/library/map:coords_set",
"//src/main/cpp/net/eagle0/shardok/library/util:hex_map_utils",
"//src/main/protobuf/net/eagle0/shardok/common:command_type_cc_proto",
],
)
cc_library(
name = "ai_command_evaluator",
srcs = ["AICommandEvaluator.cpp"],
hdrs = ["AICommandEvaluator.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai_performance_runner:__pkg__",
"//src/test/cpp/net/eagle0/shardok/ai:__pkg__",
],
deps = [
":ai_command_filter",
":ai_strategy",
":transposition_table",
"//src/main/cpp/net/eagle0/common:sequence_random_generator",
"//src/main/cpp/net/eagle0/shardok/ai/score:ai_score_calculator_interface",
"//src/main/cpp/net/eagle0/shardok/library:engine",
"//src/main/cpp/net/eagle0/shardok/library:game_state_w",
"//src/main/cpp/net/eagle0/shardok/library/map:coords_set",
"//src/main/cpp/net/eagle0/shardok/library/util:hex_cube_utils",
"//src/main/protobuf/net/eagle0/shardok/common:command_type_cc_proto",
],
)
cc_library(
name = "ai_command_filter",
srcs = ["AICommandFilter.cpp"],
hdrs = ["AICommandFilter.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai/mcts:__pkg__",
"//src/main/cpp/net/eagle0/shardok/ai/mcts/adapters:__pkg__",
"//src/main/cpp/net/eagle0/shardok/ai_performance_runner:__pkg__",
"//src/test/cpp/net/eagle0/shardok/ai:__pkg__",
],
deps = [
":ai_common_types",
"//src/main/cpp/net/eagle0/shardok/library:engine",
"//src/main/cpp/net/eagle0/shardok/library/action_point_distances:action_point_distances_cache",
"//src/main/cpp/net/eagle0/shardok/library/settings:game_settings",
"//src/main/cpp/net/eagle0/shardok/library/util:hex_map_utils",
"//src/main/flatbuffer/net/eagle0/shardok/storage:game_state_cc_fbs",
"//src/main/protobuf/net/eagle0/shardok/api:command_descriptor_cc_proto",
"//src/main/protobuf/net/eagle0/shardok/common:command_type_cc_proto",
],
)
cc_library(
name = "transposition_table",
srcs = ["TranspositionTable.cpp"],
hdrs = ["TranspositionTable.hpp"],
name = "ai_score_calculator",
srcs = ["AIScoreCalculator.cpp"],
hdrs = ["AIScoreCalculator.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai_performance_runner:__pkg__",
"//src/test/cpp/net/eagle0/shardok/ai:__pkg__",
],
deps = [
"//src/main/cpp/net/eagle0/shardok/library:game_state_w",
":ai_attacker_strategy_selector",
":ai_command_filter",
":ai_unit_score_calculator",
":ai_victory_condition_score_calculator",
"//src/main/cpp/net/eagle0/common:sequence_random_generator",
"//src/main/cpp/net/eagle0/shardok/library:engine",
"//src/main/cpp/net/eagle0/shardok/library/view_filters:game_state_guesser",
],
)
@@ -250,13 +172,11 @@ cc_library(
hdrs = ["AIStrategy.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai:__subpackages__",
"//src/main/cpp/net/eagle0/shardok/ai_performance_runner:__pkg__",
"//src/test/cpp/net/eagle0/shardok/ai:__subpackages__",
],
deps = [
":ai_attack_groups",
"//src/main/cpp/net/eagle0/shardok/library/map:coords_set",
],
)
@@ -266,7 +186,6 @@ cc_library(
hdrs = ["AIUnitScoreCalculator.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai:__subpackages__",
"//src/main/cpp/net/eagle0/shardok/ai_performance_runner:__pkg__",
"//src/test/cpp/net/eagle0/shardok/ai:__subpackages__",
],
@@ -277,6 +196,26 @@ cc_library(
],
)
cc_library(
name = "ai_victory_condition_score_calculator",
srcs = ["AIVictoryConditionScoreCalculator.cpp"],
hdrs = ["AIVictoryConditionScoreCalculator.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai_performance_runner:__pkg__",
"//src/test/cpp/net/eagle0/shardok/ai:__subpackages__",
],
deps = [
":ai_attack_groups",
":ai_attack_locations",
":ai_distance_debuf",
":ai_score_utilities",
"//src/main/cpp/net/eagle0/shardok/library/action_point_distances",
"//src/main/cpp/net/eagle0/shardok/library/action_point_distances:action_point_distances_cache",
"//src/main/flatbuffer/net/eagle0/shardok/storage:game_state_cc_fbs",
],
)
cc_library(
name = "ai_water_crossing_calculator",
srcs = ["AIWaterCrossingCalculator.cpp"],
@@ -284,14 +223,10 @@ cc_library(
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok:__pkg__",
"//src/main/cpp/net/eagle0/shardok/ai:__subpackages__",
"//src/test/cpp/net/eagle0/shardok/ai:__subpackages__",
],
deps = [
":ai_common_types",
":ai_minimum_distance_and_target",
"//src/main/cpp/net/eagle0/shardok/ai/score:ai_score_calculator_interface",
"//src/main/cpp/net/eagle0/shardok/library:game_state_w",
"//src/main/cpp/net/eagle0/shardok/library/action_point_distances",
"//src/main/cpp/net/eagle0/shardok/library/action_point_distances:action_point_distances_cache",
"//src/main/cpp/net/eagle0/shardok/library/fb_helpers:hex_map_helpers",
@@ -312,6 +247,7 @@ cc_library(
"//src/main/cpp/net/eagle0/shardok/library:game_state_w",
"//src/main/cpp/net/eagle0/shardok/library/action_point_distances",
"//src/main/cpp/net/eagle0/shardok/library/action_point_distances:action_point_distances_cache",
"//src/main/protobuf/net/eagle0/shardok/api:command_descriptor_cc_proto",
],
)
@@ -321,7 +257,6 @@ cc_library(
hdrs = ["AITimeBudget.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai/mcts:__pkg__",
"//src/main/cpp/net/eagle0/shardok/ai_performance_runner:__pkg__",
"//src/test/cpp/net/eagle0/shardok/ai:__pkg__",
],
@@ -340,31 +275,38 @@ cc_library(
hdrs = ["IterativeDeepeningAI.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai/mcts:__pkg__",
"//src/main/cpp/net/eagle0/shardok/ai_performance_runner:__pkg__",
"//src/test/cpp/net/eagle0/shardok/ai:__pkg__",
],
deps = [
":ai_attacker_strategy_selector",
":ai_command_evaluator",
":ai_defender_strategy_selector",
":ai_score_calculator",
":ai_time_budget",
":ai_water_crossing_command_chooser",
"//src/main/cpp/net/eagle0/common:time_utils",
"//src/main/cpp/net/eagle0/shardok/ai/score:ai_score_calculator_interface",
"//src/main/cpp/net/eagle0/shardok/library:engine",
"//src/main/cpp/net/eagle0/shardok/library/util:hex_map_utils",
"//src/main/protobuf/net/eagle0/shardok/api:command_descriptor_cc_proto",
],
)
cc_library(
name = "ai_config",
hdrs = ["AIConfig.hpp"],
name = "ai_flee_decision_calculator",
srcs = ["AIFleeDecisionCalculator.cpp"],
hdrs = ["AIFleeDecisionCalculator.hpp"],
copts = COPTS,
visibility = [
"//src/main/cpp/net/eagle0/shardok/ai_performance_runner:__pkg__",
"//src/test/cpp/net/eagle0/shardok/ai:__pkg__",
],
deps = [
":ai_score_utilities",
"//src/main/cpp/net/eagle0/shardok/library:engine",
"//src/main/cpp/net/eagle0/shardok/library/settings:game_settings",
"//src/main/cpp/net/eagle0/shardok/library/util:hex_map_utils",
"//src/main/protobuf/net/eagle0/shardok/api:command_descriptor_cc_proto",
],
)
cc_library(
@@ -375,21 +317,15 @@ cc_library(
visibility = ["//visibility:public"],
deps = [
":ai_attacker_strategy_selector",
":ai_config",
":ai_defender_strategy_selector",
":ai_flee_decision_calculator",
":ai_iterative_deepening", # Direct dependency for runtime selection
":ai_iterative_deepening",
":ai_score_calculator",
":ai_time_budget",
":ai_water_crossing_command_chooser",
"//src/main/cpp/net/eagle0/common:time_utils",
"//src/main/cpp/net/eagle0/shardok/ai/mcts:shardok_mcts_ai", # MCTS with abstraction layer
"//src/main/cpp/net/eagle0/shardok/ai/score:ai_score_calculator_interface",
"//src/main/cpp/net/eagle0/shardok/ai/score:mcts_optimized_ai_score_calculator", # Bounded linear scorer for MCTS
"//src/main/cpp/net/eagle0/shardok/ai/score:normalized_ai_score_calculator", # Normalized [0,1] scorer for ML training
"//src/main/cpp/net/eagle0/shardok/ai/score:standard_ai_score_calculator", # Standard unbounded scorer (default)
"//src/main/cpp/net/eagle0/shardok/library/action_point_distances",
"//src/main/cpp/net/eagle0/shardok/library/action_point_distances:action_point_distances_cache",
"//src/main/cpp/net/eagle0/shardok/library/util:game_state_dumper",
"@com_google_protobuf//:protobuf",
],
)
@@ -10,9 +10,8 @@
#include <utility>
#include "AIAttackerStrategySelector.hpp"
#include "AICommandEvaluator.hpp"
#include "TranspositionTable.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/score/AIScoreCalculator.hpp"
#include "AIScoreCalculator.hpp"
#include "src/main/cpp/net/eagle0/common/TimeUtils.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/ShardokEngine.hpp"
namespace shardok {
@@ -24,21 +23,19 @@ IterativeDeepeningAI::IterativeDeepeningAI(
const bool isDefender,
AIStrategy strategy,
const CoordsSet& castleCoords,
const AIScoreCalculator& scorer,
const APDCache& apdCache,
BattalionTypeGetter battalionTypeGetter)
const ALCache& alCache)
: playerId(playerId),
isDefender(isDefender),
strategy(std::move(strategy)),
castleCoords(castleCoords),
scorer(scorer),
apdCache(apdCache),
battalionTypeGetter(std::move(battalionTypeGetter)) {} // Move the function object
alCache(alCache) {}
auto IterativeDeepeningAI::IterativeSearch(
const GameSettingsSPtr& settings,
const GameStateW& state,
const CommandListSPtr& commands,
const std::vector<CommandProto>& commands,
const AITimeBudget& initialBudget) const -> SearchResult {
// Make a mutable copy of the time budget to track remaining time
AITimeBudget timeBudget = initialBudget;
@@ -46,12 +43,7 @@ auto IterativeDeepeningAI::IterativeSearch(
const auto initialBudgetMs = initialBudget.remainingBudget;
SearchResult result;
// Increment TT age for replacement strategy (new search)
g_transpositionTable.incrementAge();
// DEBUG: Clear TT to see if that's causing the suspicious depth reaching
// g_transpositionTable.clear(); // Uncomment to test without cross-search caching
if (commands->empty()) {
if (commands.empty()) {
#if DEBUG_ITERATIVE_DEEPENING_TIMINGS
printf("ID AI: Commands are empty, returning early\n");
#endif
@@ -59,30 +51,35 @@ auto IterativeDeepeningAI::IterativeSearch(
return result;
}
// Check if we're in SET_UP phase and enforce maximum depth limit
// Check if we're in SET_UP phase
bool isSetupPhase =
(state->status()->state() ==
net::eagle0::shardok::storage::fb::GameStatus_::State_SET_UP);
// Limit depth to prevent thread pool exhaustion and keep search reasonable
size_t maxDepth = isSetupPhase ? 2 : 8;
int maxDepth = isSetupPhase ? 2 : std::numeric_limits<int>::max();
// Calculate current utility and create engine once for all command evaluations
const auto& settingsGetter = settings->GetGetter();
const auto guessedEngine = ShardokEngine(settings, state);
const auto maxRepeatCount = settingsGetter.Backing().ai_utility_repeat_count();
const ScoreValue currentUtility =
scorer.GuessedStateScore(isDefender, state, strategy, castleCoords);
const ScoreValue currentUtility = AIScoreCalculator::GuessedStateScore(
isDefender,
state,
strategy,
castleCoords,
settingsGetter,
apdCache,
alCache);
// Initialize data structures for tracking scores at each depth
scoresByDepth.clear();
scoresByDepth.resize(commands->size());
scoresByDepth.resize(commands.size());
highestDepthCompleted.clear();
highestDepthCompleted.resize(commands->size(), 0);
highestDepthCompleted.resize(commands.size(), 0);
size_t currentDepth = 1;
int currentDepth = 1;
size_t previousBestCommand = 0; // Track best command from previous depth
size_t evaluatedCountAtHighestDepth = 0;
auto completionReason = EvaluationCompletionReason::RAN_OUT_OF_TIME;
EvaluationCompletionReason completionReason = EvaluationCompletionReason::RAN_OUT_OF_TIME;
// Main iterative deepening loop
while ((currentDepth == 1 || !IsTimeExpired(timeBudget)) && currentDepth <= maxDepth) {
@@ -92,37 +89,27 @@ auto IterativeDeepeningAI::IterativeSearch(
scoresByDepth,
highestDepthCompleted);
size_t evaluatedCount = 0;
int evaluatedCount = 0;
bool allEvaluated = true;
bool allEndTurnCommands = true; // Track if all commands are END_TURN
// Start all command evaluations for this depth
std::vector<std::pair<size_t, std::future<SearchResult>>> futures;
futures.reserve(sortedIndices.size());
// Try to evaluate all commands at this depth, within budget constraints
for (size_t cmdIndex : sortedIndices) {
if (currentDepth > 1 && IsTimeExpired(timeBudget)) {
allEvaluated = false;
break;
}
auto future = SearchCommandAtDepthWithEngine(
auto cmdResult = SearchCommandAtDepthWithEngine(
guessedEngine,
scorer,
settingsGetter,
maxRepeatCount,
commands,
cmdIndex,
currentDepth, // Pass current iteration depth as desired search depth
currentDepth,
currentUtility,
timeBudget);
futures.emplace_back(cmdIndex, std::move(future));
}
// Now wait for all futures and collect results
for (auto& [cmdIndex, future] : futures) {
auto cmdResult = future.get();
// Ensure scoresByDepth[cmdIndex] has enough space
if (scoresByDepth[cmdIndex].size() <= currentDepth) {
scoresByDepth[cmdIndex].resize(currentDepth + 1);
@@ -132,8 +119,7 @@ auto IterativeDeepeningAI::IterativeSearch(
evaluatedCount++;
// Check if this command is not END_TURN_COMMAND
if ((*commands)[cmdIndex]->GetCommandType() !=
net::eagle0::shardok::common::END_TURN_COMMAND) {
if (commands[cmdIndex].type() != net::eagle0::shardok::common::END_TURN_COMMAND) {
allEndTurnCommands = false;
}
}
@@ -144,7 +130,7 @@ auto IterativeDeepeningAI::IterativeSearch(
size_t currentBestCommand = 0;
ScoreValue currentBestScore = -std::numeric_limits<ScoreValue>::infinity();
for (size_t i = 0; i < commands->size(); ++i) {
for (size_t i = 0; i < commands.size(); ++i) {
if (highestDepthCompleted[i] >= currentDepth) {
if (scoresByDepth[i][currentDepth] > currentBestScore) {
currentBestScore = scoresByDepth[i][currentDepth];
@@ -156,21 +142,17 @@ auto IterativeDeepeningAI::IterativeSearch(
// Log if best command changed from previous depth
if (currentDepth > 1 && currentBestCommand != previousBestCommand) {
#if DEBUG_ITERATIVE_DEEPENING_TIMINGS
printf("ID AI: Best command changed at depth %lu:\n", currentDepth);
printf(" Depth %lu best: command %zu (score %.2f) - type: %s\n",
printf("ID AI: Best command changed at depth %d:\n", currentDepth);
printf(" Depth %d best: command %zu (score %.2f) - %s\n",
currentDepth - 1,
previousBestCommand,
scoresByDepth[previousBestCommand][currentDepth - 1],
net::eagle0::shardok::common::CommandType_Name(
(*commands)[previousBestCommand]->GetCommandType())
.c_str());
printf(" Depth %lu best: command %zu (score %.2f) - type: %s\n",
commands[previousBestCommand].DebugString().c_str());
printf(" Depth %d best: command %zu (score %.2f) - %s\n",
currentDepth,
currentBestCommand,
currentBestScore,
net::eagle0::shardok::common::CommandType_Name(
(*commands)[currentBestCommand]->GetCommandType())
.c_str());
commands[currentBestCommand].DebugString().c_str());
#endif
}
@@ -193,12 +175,12 @@ auto IterativeDeepeningAI::IterativeSearch(
// This indicates we've hit END_TURN in the lookahead
if (currentDepth > 1 && evaluatedCount > 0) {
bool scoresUnchanged = true;
size_t unchangedCount = 0;
int unchangedCount = 0;
for (size_t i = 0; i < sortedIndices.size() && i < evaluatedCount; ++i) {
size_t cmdIndex = sortedIndices[i];
// This command was evaluated at both current and previous depth
if (size_t cmdIndex = sortedIndices[i];
scoresByDepth[cmdIndex].size() > currentDepth &&
if (scoresByDepth[cmdIndex].size() > currentDepth &&
scoresByDepth[cmdIndex].size() > currentDepth - 1) {
// Check if score changed between depth N-1 and depth N
if (std::abs(
@@ -222,11 +204,10 @@ auto IterativeDeepeningAI::IterativeSearch(
// Check if we've used more than 50% of total budget
auto totalElapsed = std::chrono::steady_clock::now() - startTime;
auto totalElapsedMs = std::chrono::duration_cast<std::chrono::milliseconds>(totalElapsed);
double budgetUsedPercent = static_cast<double>(totalElapsedMs.count()) /
static_cast<double>(initialBudgetMs.count());
double budgetUsedPercent = (double)totalElapsedMs.count() / initialBudgetMs.count();
if (budgetUsedPercent > 0.5) {
printf("ID AI: Stopping after depth %lu - used %.1f%% of time budget\n",
printf("ID AI: Stopping after depth %d - used %.1f%% of time budget\n",
currentDepth,
budgetUsedPercent * 100);
completionReason = EvaluationCompletionReason::NOT_ENOUGH_TIME_TO_CONTINUE;
@@ -249,7 +230,7 @@ auto IterativeDeepeningAI::IterativeSearch(
result.searchCompleted = result.minimumDepthCompleted;
result.timeUsed = std::chrono::duration_cast<std::chrono::milliseconds>(
std::chrono::steady_clock::now() - startTime);
result.availableCommandCount = commands->size();
result.availableCommandCount = commands.size();
result.commandCountEvaluated = evaluatedCountAtHighestDepth;
result.completionReason = completionReason;
@@ -261,8 +242,6 @@ auto IterativeDeepeningAI::IterativeSearch(
result.availableCommandCount);
}
// Print TranspositionTable statistics
g_transpositionTable.printStats();
return result;
}
@@ -272,79 +251,72 @@ bool IterativeDeepeningAI::IsTimeExpired(const AITimeBudget& budget) {
auto IterativeDeepeningAI::SearchCommandAtDepthWithEngine(
const ShardokEngine& guessedEngine,
const AIScoreCalculator& scorer,
const GameSettings::Getter& settingsGetter,
const int maxRepeatCount,
const CommandListSPtr& commands,
const std::vector<CommandProto>& commands,
const size_t commandIndex,
const int desiredDepth,
const int depth,
const ScoreValue currentUtility,
AITimeBudget& timeBudget) const -> std::future<SearchResult> {
AITimeBudget& timeBudget) const -> SearchResult {
SearchResult result;
result.bestCommandIndex = commandIndex;
result.depthAchieved = desiredDepth;
result.depthAchieved = depth;
result.searchCompleted = true;
result.minimumDepthCompleted = true;
result.availableCommandCount = commands->size();
result.availableCommandCount = commands.size();
result.commandCountEvaluated = 1; // We're evaluating just this command
if (commandIndex >= commands->size()) {
if (commandIndex >= commands.size()) {
result.bestScore = 0.0;
std::promise<SearchResult> p;
p.set_value(result);
return p.get_future();
return result;
}
// Track concurrent evaluations and adjust time accounting
AIEvaluationCounter counter;
const auto startTime = std::chrono::steady_clock::now();
try {
// Track concurrent evaluations and adjust time accounting
AIEvaluationCounter counter;
const auto startTime = std::chrono::steady_clock::now();
// Calculate deadline from remaining time budget
const auto deadline = startTime + timeBudget.remainingBudget;
// Use CommandScore to evaluate the specific command at the given depth
const auto commandScore = AIScoreCalculator::CommandScore(
playerId,
isDefender,
depth,
maxRepeatCount,
guessedEngine,
strategy,
currentUtility,
settingsGetter,
castleCoords,
apdCache,
alCache,
commandIndex);
// Create command evaluator for lookahead search
AICommandEvaluator evaluator(scorer, apdCache, battalionTypeGetter);
// Calculate time used and adjust based on concurrent evaluations
const auto elapsed = std::chrono::steady_clock::now() - startTime;
const int concurrentCount = counter.GetCurrentCount();
const auto adjustedElapsed = elapsed / std::max(1, concurrentCount);
const auto adjustedElapsedMs =
std::chrono::duration_cast<std::chrono::milliseconds>(adjustedElapsed);
// Get the future from EvaluateCommand - don't wait yet
// Note: EvaluateCommand expects remainingLookahead, not desiredDepth
// desiredDepth 1 = evaluate immediate (remainingLookahead 0)
// desiredDepth 2 = look 1 move ahead (remainingLookahead 1)
// desiredDepth N = look N-1 moves ahead (remainingLookahead N-1)
auto commandScoreFuture = evaluator.EvaluateCommand(
playerId,
isDefender,
desiredDepth - 1, // Convert desiredDepth to remainingLookahead
maxRepeatCount,
guessedEngine,
strategy,
currentUtility,
castleCoords,
commandIndex,
deadline);
// Deduct adjusted time from remaining budget
timeBudget.remainingBudget -= adjustedElapsedMs;
// Calculate time and adjust budget before waiting
// This is needed because we need to update timeBudget synchronously
const auto commandScore = commandScoreFuture.get();
result.bestScore = commandScore;
} catch (const std::exception& e) {
// If evaluation fails, return a neutral score rather than crashing
#if DEBUG_ITERATIVE_DEEPENING_TIMINGS
printf("SearchCommandAtDepthWithEngine: evaluation failed with exception: %s\n", e.what());
#endif
result.bestScore = 0.0;
}
const auto elapsed = std::chrono::steady_clock::now() - startTime;
const int concurrentCount = AIEvaluationCounter::GetCurrentCount();
const auto adjustedElapsed = elapsed / std::max(1, concurrentCount);
const auto adjustedElapsedMs =
std::chrono::duration_cast<std::chrono::milliseconds>(adjustedElapsed);
// Deduct adjusted time from remaining budget
timeBudget.remainingBudget -= adjustedElapsedMs;
result.bestScore = commandScore;
std::promise<SearchResult> p;
p.set_value(result);
return p.get_future();
return result;
}
auto IterativeDeepeningAI::GetCommandsSortedByPreviousDepth(
const size_t currentDepth,
int currentDepth,
const std::vector<std::vector<ScoreValue>>& scoresByDepth,
const std::vector<size_t>& highestDepthCompleted) -> std::vector<size_t> {
const std::vector<int>& highestDepthCompleted) const -> std::vector<size_t> {
std::vector<size_t> indices(scoresByDepth.size());
std::iota(indices.begin(), indices.end(), 0);
@@ -354,21 +326,11 @@ auto IterativeDeepeningAI::GetCommandsSortedByPreviousDepth(
}
// Sort by score at previous depth
const size_t prevDepth = currentDepth - 1;
std::ranges::sort(indices, [&](const size_t a, const size_t b) {
// Bounds check - if indices are out of range, or inner vectors are too small, treat as not
// evaluated
if (a >= scoresByDepth.size() || b >= scoresByDepth.size() ||
a >= highestDepthCompleted.size() || b >= highestDepthCompleted.size()) {
return a < b; // Maintain stable order for out-of-bounds indices
}
// Check if the scores for previous depth exist
int prevDepth = currentDepth - 1;
std::sort(indices.begin(), indices.end(), [&](size_t a, size_t b) {
// Only consider commands that were evaluated at previous depth
if (highestDepthCompleted[a] >= prevDepth && highestDepthCompleted[b] >= prevDepth) {
// Additional safety check for inner vector size
if (scoresByDepth[a].size() > prevDepth && scoresByDepth[b].size() > prevDepth) {
return scoresByDepth[a][prevDepth] > scoresByDepth[b][prevDepth];
}
return scoresByDepth[a][prevDepth] > scoresByDepth[b][prevDepth];
}
// Commands not evaluated at prev depth go to the end
return highestDepthCompleted[a] >= prevDepth;
@@ -379,7 +341,7 @@ auto IterativeDeepeningAI::GetCommandsSortedByPreviousDepth(
auto IterativeDeepeningAI::SelectBestResult(
const std::vector<std::vector<ScoreValue>>& scoresByDepth,
const std::vector<size_t>& highestDepthCompleted) -> SearchResult {
const std::vector<int>& highestDepthCompleted) const -> SearchResult {
SearchResult result;
result.bestScore = -std::numeric_limits<ScoreValue>::infinity();
result.searchCompleted = false;
@@ -387,8 +349,9 @@ auto IterativeDeepeningAI::SelectBestResult(
// Find the command with best score at its highest evaluated depth
for (size_t i = 0; i < scoresByDepth.size(); ++i) {
if (highestDepthCompleted[i] > 0) {
const size_t depth = highestDepthCompleted[i];
if (ScoreValue score = scoresByDepth[i][depth]; score > result.bestScore) {
int depth = highestDepthCompleted[i];
ScoreValue score = scoresByDepth[i][depth];
if (score > result.bestScore) {
result.bestScore = score;
result.bestCommandIndex = i;
result.depthAchieved = depth;
@@ -6,24 +6,22 @@
#define EAGLE0_ITERATIVEDEEPENINGAI_HPP
#include <chrono>
#include <future>
#include <vector>
#include "AIStrategy.hpp"
#include "AITimeBudget.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AIAttackLocations.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/score/AIScoreCalculator.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/ShardokCTypes.h"
#include "src/main/cpp/net/eagle0/shardok/library/ShardokCommand.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/action_point_distances/ActionPointDistancesCache.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/util/HexMapUtils.hpp"
#include "src/main/protobuf/net/eagle0/shardok/api/command_descriptor.pb.h"
namespace shardok {
// Forward declarations
class ShardokEngine;
using ScoreValue = double;
using BattalionTypeGetter = std::function<BattalionTypeSPtr(BattalionTypeId)>;
using CommandProto = net::eagle0::shardok::api::CommandDescriptor;
/// Reason why AI evaluation completed at the achieved depth.
enum class EvaluationCompletionReason {
@@ -37,7 +35,7 @@ public:
struct SearchResult {
size_t bestCommandIndex;
ScoreValue bestScore;
size_t depthAchieved;
int depthAchieved;
std::chrono::milliseconds timeUsed;
bool minimumDepthCompleted;
bool searchCompleted;
@@ -62,50 +60,48 @@ public:
bool isDefender,
AIStrategy strategy,
const CoordsSet& castleCoords,
const AIScoreCalculator& scorer,
const APDCache& apdCache,
BattalionTypeGetter battalionTypeGetter); // Pass by value
const ALCache& alCache);
[[nodiscard]] SearchResult IterativeSearch(
const GameSettingsSPtr& settings,
const GameStateW& state,
const CommandListSPtr& commands,
const AITimeBudget& initialBudget) const;
const std::vector<CommandProto>& commands,
const AITimeBudget& timeBudget) const;
private:
PlayerId playerId;
bool isDefender;
AIStrategy strategy;
CoordsSet castleCoords;
const AIScoreCalculator& scorer;
const APDCache& apdCache;
BattalionTypeGetter battalionTypeGetter; // Store by value, not reference!
const ALCache& alCache;
// Reusable vectors to reduce memory allocations
mutable std::vector<std::vector<ScoreValue>> scoresByDepth;
mutable std::vector<size_t> highestDepthCompleted;
mutable std::vector<int> highestDepthCompleted;
mutable std::vector<size_t> reusableSortedIndices;
[[nodiscard]] static bool IsTimeExpired(const AITimeBudget& budget);
[[nodiscard]] std::future<SearchResult> SearchCommandAtDepthWithEngine(
[[nodiscard]] SearchResult SearchCommandAtDepthWithEngine(
const ShardokEngine& guessedEngine,
const AIScoreCalculator& scorer,
const GameSettings::Getter& settingsGetter,
int maxRepeatCount,
const CommandListSPtr& commands,
const std::vector<CommandProto>& commands,
size_t commandIndex,
int desiredDepth,
int depth,
ScoreValue currentUtility,
AITimeBudget& timeBudget) const;
[[nodiscard]] static std::vector<size_t> GetCommandsSortedByPreviousDepth(
size_t currentDepth,
[[nodiscard]] std::vector<size_t> GetCommandsSortedByPreviousDepth(
int currentDepth,
const std::vector<std::vector<ScoreValue>>& scoresByDepth,
const std::vector<size_t>& highestDepthCompleted);
const std::vector<int>& highestDepthCompleted) const;
[[nodiscard]] static SearchResult SelectBestResult(
[[nodiscard]] SearchResult SelectBestResult(
const std::vector<std::vector<ScoreValue>>& scoresByDepth,
const std::vector<size_t>& highestDepthCompleted);
const std::vector<int>& highestDepthCompleted) const;
};
} // namespace shardok
@@ -10,27 +10,15 @@
#define DEBUG_FLEE_DECISIONS
// Enable to dump game state and debug tree to /tmp for debugging
// #define ENABLE_MCTS_DEBUG_DUMP
#ifdef ENABLE_MCTS_DEBUG_DUMP
#include <chrono>
#include <fstream>
#include <iomanip>
#include <sstream>
#endif
#include <google/protobuf/util/message_differencer.h>
#include "AIAttackerStrategySelector.hpp"
#include "AIConfig.hpp"
#include "AIDefenderStrategySelector.hpp"
#include "AIFleeDecisionCalculator.hpp"
#include "AIScoreUtilities.hpp"
#include "AITimeBudget.hpp"
#include "IterativeDeepeningAI.hpp"
#include "mcts/ShardokMCTSAI.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/score/MCTSOptimizedAIScoreCalculator.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/score/NormalizedAIScoreCalculator.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/score/StandardAIScoreCalculator.hpp"
#include "src/main/cpp/net/eagle0/common/TimeUtils.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/util/HexMapUtils.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/view_filters/GameStateGuesser.hpp"
#include "src/main/protobuf/net/eagle0/shardok/api/action_result_view.pb.h"
@@ -42,7 +30,7 @@ using net::eagle0::shardok::api::GameStateView;
static constexpr bool kPerformanceLogging = true;
void ApplyUpdate(GameStateView & /*currentView*/, const ActionResultView & /*update*/) {}
void ApplyUpdate(GameStateView &currentView, const ActionResultView &update) {}
auto RoundsRemaining(const GameSettingsSPtr &settings, const GameStateView &gsv) -> int {
const int maxRounds = settings->GetGetter().Backing().max_rounds();
@@ -54,17 +42,11 @@ ShardokAIClient::ShardokAIClient(
const PlayerId playerId,
const bool isDefender,
const HexMap *hexMap,
const SettingsGetter &settings,
const AIAlgorithmType aiAlgorithmType,
const ScoringCalculatorType scoringCalculatorType,
const mcts::MCTSConfig &mctsConfig)
const SettingsGetter &settings)
: playerId(playerId),
isDefender(isDefender),
aiAlgorithmType(aiAlgorithmType),
scoringCalculatorType(scoringCalculatorType),
alCache(std::make_unique<AttackLocationsCache>(hexMap, settings)),
waterCrossingCommandChooser(playerId, apdCache),
mctsConfig(mctsConfig) {
waterCrossingCommandChooser(playerId, apdCache) {
// Pre-generate the most common cache entries for better performance
const auto mapId = ActionPointDistancesCache::GetMapId(hexMap);
@@ -88,110 +70,38 @@ ShardokAIClient::ShardokAIClient(
apdCache->ConsolidateThreadLocalCache_Racy();
}
void CheckCommand(const CommandSPtr &realCommand, const CommandSPtr &guessedCommand) {
// Verify that the AI's guessed state produces the same available commands as the real state.
// We only compare fields that uniquely identify a command - metadata fields like action_points,
// will_unhide, next_round_target_info are not part of command identity.
void CheckCommand(const CommandProto &realDescriptor, const CommandProto &guessedDescriptor) {
string diff;
auto differencer = google::protobuf::util::MessageDifferencer();
differencer.IgnoreField(CommandProto::descriptor()->FindFieldByNumber(
CommandProto::kFollowUpCommandTypesFieldNumber));
differencer.ReportDifferencesToString(&diff);
if (!differencer.Compare(realDescriptor, guessedDescriptor)) {
printf("diff: %s\n\n", diff.c_str());
if (realCommand->GetCommandType() != guessedCommand->GetCommandType()) {
throw ShardokInternalErrorException("Command type mismatch between real and guessed state");
}
if (realCommand->GetPlayerId() != guessedCommand->GetPlayerId()) {
throw ShardokInternalErrorException("Player ID mismatch between real and guessed state");
}
if (realCommand->GetActorUnitId() != guessedCommand->GetActorUnitId()) {
throw ShardokInternalErrorException("Actor unit mismatch between real and guessed state");
}
if (realCommand->GetTargetRow() != guessedCommand->GetTargetRow() ||
realCommand->GetTargetColumn() != guessedCommand->GetTargetColumn()) {
throw ShardokInternalErrorException(
"Target coordinates mismatch between real and guessed state");
}
// For commands with odds (like FLEE), verify the odds match
if (realCommand->HasOdds() != guessedCommand->HasOdds()) {
throw ShardokInternalErrorException(
"Odds presence mismatch between real and guessed state");
}
if (realCommand->HasOdds() && guessedCommand->HasOdds()) {
if (realCommand->GetOddsPercentile() != guessedCommand->GetOddsPercentile()) {
throw ShardokInternalErrorException(
"Odds percentile mismatch between real and guessed state");
}
printf("Selected command descriptor\n%s\ndoes not match guessed\n%s\n\n",
realDescriptor.DebugString().c_str(),
guessedDescriptor.DebugString().c_str());
throw ShardokInternalErrorException("Illegal state for AI client");
}
}
auto ShardokAIClient::StandardChooseCommandIndex(
const GameSettingsSPtr &settings,
const GameStateW &guessedState,
const CommandListSPtr &realAvailableCommands) const -> CommandChoiceResults {
const vector<CommandProto> &realAvailableCommands) const -> CommandChoiceResults {
const auto settingsGetter = settings->GetGetter();
const auto guessedEngine = ShardokEngine(settings, guessedState);
const auto guessedCommands = guessedEngine.GetAvailableCommandsForAIPlayer(playerId);
const auto commandCount = guessedCommands->size();
// Calculate time budget based on game situation using new dynamic per-command settings
const auto timeBudget = CalculateTimeBudget(playerId, settings, guessedState, commandCount);
// Calculate time budget based on game situation using new settings
const auto timeBudget = CalculateTimeBudget(playerId, settings, guessedState);
// Configure MCTS based on proximity to enemy
// When far from enemy: use AVERAGING with maxPlayerFlips=0 (single-player lookahead)
// - AVERAGING naturally penalizes longer paths through variance
// - No opponent nodes, so no one-bad-child problem
// When close to enemy: use MINIMAX with maxPlayerFlips=1 (adversarial lookahead)
// - MINIMAX correctly models opponent choosing best response
// - Explores through one opponent turn for tactical accuracy
auto adjustedMCTSConfig = mctsConfig;
const auto guessedCommands = guessedEngine.GetAvailableCommandProtos(playerId, false);
const auto commandCount = guessedCommands.size();
// For fair evaluation: simulate leaves to opponent's turn start (maxSimulationFlips=1)
// This ensures all leaves are scored at the same game phase:
// - Leaves at playerFlips=0 (still my turn): simulate through END_TURN to playerFlips=1
// - Leaves at playerFlips=1 (opponent's turn): evaluate immediately
// Result: consistent comparison of "what happens after I end my turn"
// adjustedMCTSConfig.maxSimulatfixionFlips = 1;
// adjustedMCTSConfig.maxPlayerFlips = 0;
// if (timeBudget.isCloseToEnemy) {
// adjustedMCTSConfig.maxPlayerFlips = 1;
// adjustedMCTSConfig.backpropagationPolicy = mcts::MCTSBackpropagationPolicy::MINIMAX;
// if constexpr (kPerformanceLogging) {
// printf("MCTS Config: Close to enemy - using maxPlayerFlips=1, MINIMAX backprop\n");
// }
// } else {
// adjustedMCTSConfig.maxPlayerFlips = 0;
// adjustedMCTSConfig.backpropagationPolicy = mcts::MCTSBackpropagationPolicy::AVERAGING;
// if constexpr (kPerformanceLogging) {
// printf("MCTS Config: Far from enemy - using maxPlayerFlips=0, AVERAGING backprop\n");
// }
// }
assert(commandCount == realAvailableCommands->size());
// Verify that the AI's guessed state produces the same available commands as reality
for (size_t i = 0; i < commandCount; i++) {
CheckCommand((*realAvailableCommands)[i], (*guessedCommands)[i]);
}
// Extract values directly from settings for strategy selection
const auto maxRounds = settingsGetter.Backing().max_rounds();
const auto braveWaterCost = settingsGetter.Backing().brave_water_action_point_cost();
const auto battalionTypeGetter = [&settingsGetter](BattalionTypeId typeId) {
return settingsGetter.GetBattalionType(typeId);
};
// Create scorer for actual scoring during search - type selected at construction
std::unique_ptr<AIScoreCalculator> scorer;
switch (scoringCalculatorType) {
case ScoringCalculatorType::NORMALIZED:
scorer = MakeNormalizedAIScoreCalculator(settingsGetter, apdCache, alCache);
break;
case ScoringCalculatorType::MCTS_OPTIMIZED:
scorer = MakeMCTSOptimizedAIScoreCalculator(settingsGetter, apdCache, alCache);
break;
case ScoringCalculatorType::STANDARD:
default: scorer = MakeStandardAIScoreCalculator(settingsGetter, apdCache, alCache); break;
assert(commandCount == realAvailableCommands.size());
for (int i = 0; i < commandCount; i++) {
CheckCommand(realAvailableCommands[i], guessedCommands[i]);
}
// Determine strategy once for consistent scoring throughout iterative deepening
@@ -199,80 +109,23 @@ auto ShardokAIClient::StandardChooseCommandIndex(
const AIStrategy strategy = isDefender ? AIDefenderStrategySelector::BestDefenderStrategy(
guessedState,
castleCoords,
maxRounds,
apdCache,
battalionTypeGetter)
settingsGetter)
: AIAttackerStrategySelector::BestAttackerStrategy(
playerId,
guessedState,
castleCoords,
maxRounds,
apdCache,
alCache,
battalionTypeGetter,
braveWaterCost,
settingsGetter,
waterCrossingCommandChooser,
realAvailableCommands);
// AI implementation chosen at runtime via constructor parameter
IterativeDeepeningAI::SearchResult search_result;
if (aiAlgorithmType == AIAlgorithmType::MCTS) {
#ifdef ENABLE_MCTS_DEBUG_DUMP
// Set unique debug dump path for each action using timestamp
const auto now = std::chrono::system_clock::now();
const auto nowTime = std::chrono::system_clock::to_time_t(now);
const auto nowMs =
std::chrono::duration_cast<std::chrono::milliseconds>(now.time_since_epoch()) %
1000;
std::ostringstream pathStream;
pathStream << "/tmp/shardok_debug_"
<< std::put_time(std::localtime(&nowTime), "%Y%m%d_%H%M%S") << "_"
<< std::setfill('0') << std::setw(3) << nowMs.count() << "_p"
<< static_cast<int>(playerId) << ".txt";
adjustedMCTSConfig.debugDumpPath = pathStream.str();
// Also dump the game state to a file for reproduction
std::ostringstream statePathStream;
statePathStream << "/tmp/shardok_state_"
<< std::put_time(std::localtime(&nowTime), "%Y%m%d_%H%M%S") << "_"
<< std::setfill('0') << std::setw(3) << nowMs.count() << "_p"
<< static_cast<int>(playerId) << ".bin";
const std::string statePath = statePathStream.str();
// Write the flatbuffer game state to file using SaveTo method
if (guessedState.SaveTo(statePath)) {
printf("Game state dumped to: %s\n", statePath.c_str());
} else {
printf("Failed to dump game state to: %s\n", statePath.c_str());
}
#endif // ENABLE_MCTS_DEBUG_DUMP
// Using Monte Carlo Tree Search AI (with abstraction layer)
ShardokMCTSAI ai(
playerId,
isDefender,
strategy,
castleCoords,
*scorer,
apdCache,
alCache,
adjustedMCTSConfig);
search_result = ai.Search(settings, guessedState, timeBudget);
} else {
// Using Iterative Deepening AI (default)
IterativeDeepeningAI ai(
playerId,
isDefender,
strategy,
castleCoords,
*scorer,
apdCache,
battalionTypeGetter);
search_result =
ai.IterativeSearch(settings, guessedState, realAvailableCommands, timeBudget);
}
// Use iterative deepening AI for Phase 2 implementation
IterativeDeepeningAI
iterativeAI(playerId, isDefender, strategy, castleCoords, apdCache, alCache);
auto search_result =
iterativeAI.IterativeSearch(settings, guessedState, realAvailableCommands, timeBudget);
CommandChoiceResults result{};
result.chosenIndex = search_result.bestCommandIndex;
@@ -288,12 +141,9 @@ auto ShardokAIClient::StandardChooseCommandIndex(
result.commandCountEvaluated,
result.availableCommandCount);
}
const auto chosenCommandType =
(*realAvailableCommands)[result.chosenIndex]->GetCommandType();
printf("ID AI: Search complete - achieved depth %d for best command %zu (%s)\n",
printf("ID AI: Search complete - achieved depth %d for best command %zu\n",
result.depthAchieved,
result.chosenIndex,
net::eagle0::shardok::common::CommandType_Name(chosenCommandType).c_str());
result.chosenIndex);
fflush(stdout);
}
@@ -304,20 +154,19 @@ auto ShardokAIClient::StandardChooseCommandIndex(
auto ShardokAIClient::LateRoundAttackerChooseCommandIndex(
const GameSettingsSPtr &settings,
const GameStateW &guessedState,
const CommandListSPtr &realAvailableCommands) const -> CommandChoiceResults {
const vector<CommandProto> &realAvailableCommands) const -> CommandChoiceResults {
if (const auto dismissCommand = std::ranges::find_if(
*realAvailableCommands,
[](const CommandSPtr &cmd) {
return cmd->GetCommandType() ==
net::eagle0::shardok::common::DISMISS_UNIT_COMMAND;
realAvailableCommands,
[](const net::eagle0::shardok::api::CommandDescriptor &cmd) {
return cmd.type() == net::eagle0::shardok::common::DISMISS_UNIT_COMMAND;
});
dismissCommand == realAvailableCommands->end()) {
dismissCommand == realAvailableCommands.end()) {
return StandardChooseCommandIndex(settings, guessedState, realAvailableCommands);
} else {
CommandChoiceResults results{};
results.chosenIndex =
static_cast<size_t>(std::distance(realAvailableCommands->begin(), dismissCommand));
results.availableCommandCount = realAvailableCommands->size();
static_cast<size_t>(std::distance(realAvailableCommands.begin(), dismissCommand));
results.availableCommandCount = realAvailableCommands.size();
results.depthAchieved = 1; // Simple heuristic choice
results.commandCountEvaluated = 1; // Only evaluated one command type
results.completionReason =
@@ -329,31 +178,24 @@ auto ShardokAIClient::LateRoundAttackerChooseCommandIndex(
auto ShardokAIClient::FinalRoundAttackerChooseCommandIndex(
const GameSettingsSPtr &settings,
const GameStateW &guessedState,
const CommandListSPtr &realAvailableCommands) const -> CommandChoiceResults {
const auto fleeCommand =
std::ranges::find_if(*realAvailableCommands, [](const CommandSPtr &cmd) {
return cmd->GetCommandType() == net::eagle0::shardok::common::FLEE_COMMAND;
const vector<CommandProto> &realAvailableCommands) const -> CommandChoiceResults {
const auto fleeCommand = std::ranges::find_if(
realAvailableCommands,
[](const net::eagle0::shardok::api::CommandDescriptor &cmd) {
return cmd.type() == net::eagle0::shardok::common::FLEE_COMMAND;
});
if (fleeCommand == realAvailableCommands->end()) {
if (fleeCommand == realAvailableCommands.end()) {
return LateRoundAttackerChooseCommandIndex(settings, guessedState, realAvailableCommands);
}
// Extract values directly from settings for flee decision evaluation
const auto settingsGetter = settings->GetGetter();
const auto maxRounds = settingsGetter.Backing().max_rounds();
const auto minimumFleeOddsThreshold = settingsGetter.Backing().ai_minimum_flee_odds_threshold();
const auto desperateFleeThreshold = settingsGetter.Backing().ai_desperate_flee_threshold();
// Use the flee decision calculator
const auto fleeDecision = AIFleeDecisionCalculator::EvaluateFleeVsFight(
playerId,
settings,
guessedState,
realAvailableCommands,
fleeCommand,
maxRounds,
minimumFleeOddsThreshold,
desperateFleeThreshold,
#ifdef DEBUG_FLEE_DECISIONS
true // Enable debug logging
#else
@@ -364,7 +206,7 @@ auto ShardokAIClient::FinalRoundAttackerChooseCommandIndex(
if (fleeDecision.shouldFlee) {
CommandChoiceResults results{};
results.chosenIndex = fleeDecision.commandIndex;
results.availableCommandCount = realAvailableCommands->size();
results.availableCommandCount = realAvailableCommands.size();
results.depthAchieved = 1; // Heuristic choice
results.commandCountEvaluated = 1; // Only evaluated one command type
results.completionReason = EvaluationCompletionReason::RAN_OUT_OF_COMMANDS;
@@ -378,7 +220,7 @@ auto ShardokAIClient::FinalRoundAttackerChooseCommandIndex(
auto ShardokAIClient::ChooseCommandIndex(
const GameSettingsSPtr &settings,
const GameStateView &gsv,
const CommandListSPtr &realAvailableCommands) const -> CommandChoiceResults {
const vector<CommandProto> &realAvailableCommands) const -> CommandChoiceResults {
static int typeChosenCount[net::eagle0::shardok::common::CommandType_MAX + 1];
static int totalChoices = 0;
@@ -397,7 +239,7 @@ auto ShardokAIClient::ChooseCommandIndex(
results = StandardChooseCommandIndex(settings, guessedState, realAvailableCommands);
}
const auto chosenType = (*realAvailableCommands)[results.chosenIndex]->GetCommandType();
const auto chosenType = realAvailableCommands[results.chosenIndex].type();
typeChosenCount[static_cast<int>(chosenType)]++;
totalChoices++;
@@ -423,8 +265,8 @@ auto ShardokAIClient::ChooseCommandIndex(
auto ShardokAIClient::ChooseCommandIndex(const ShardokEngine &engine) const
-> CommandChoiceResults {
if (const auto &availableCommands = engine.GetAvailableCommandsForAIPlayer(playerId);
availableCommands->empty()) {
if (const auto &availableCommands = engine.GetAvailableCommandProtos(playerId, false);
availableCommands.empty()) {
printf("no commands for player %d\n", playerId);
throw ShardokInternalErrorException(
"Asked to choose a command, but there are none available");
@@ -12,13 +12,10 @@
#include <vector>
#include "src/main/cpp/net/eagle0/common/RandomGenerator.hpp"
#include "src/main/cpp/net/eagle0/common/mcts/abstract/MCTSTypes.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AIConfig.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AIScoreCalculator.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AITimeBudget.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/AIWaterCrossingCommandChooser.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/IterativeDeepeningAI.hpp"
#include "src/main/cpp/net/eagle0/shardok/ai/score/AIScoreCalculator.hpp"
#include "src/main/cpp/net/eagle0/shardok/library/ShardokCommand.hpp"
#include "src/main/protobuf/net/eagle0/shardok/api/game_state_view.pb.h"
namespace shardok {
@@ -41,55 +38,42 @@ class ShardokAIClient {
private:
const PlayerId playerId;
const bool isDefender;
const AIAlgorithmType aiAlgorithmType;
const ScoringCalculatorType scoringCalculatorType;
APDCache apdCache = std::make_shared<ActionPointDistancesCache>();
ALCache alCache;
const AIWaterCrossingCommandChooser waterCrossingCommandChooser;
// MCTS configuration (only used when aiAlgorithmType == MCTS)
mcts::MCTSConfig mctsConfig;
[[nodiscard]] auto StandardChooseCommandIndex(
const GameSettingsSPtr& settings,
const GameStateW& guessedState,
const CommandListSPtr& realAvailableCommands) const -> CommandChoiceResults;
const vector<CommandProto>& realAvailableCommands) const -> CommandChoiceResults;
[[nodiscard]] auto LateRoundAttackerChooseCommandIndex(
const GameSettingsSPtr& settings,
const GameStateW& guessedState,
const CommandListSPtr& realAvailableCommands) const -> CommandChoiceResults;
const vector<CommandProto>& realAvailableCommands) const -> CommandChoiceResults;
[[nodiscard]] auto FinalRoundAttackerChooseCommandIndex(
const GameSettingsSPtr& settings,
const GameStateW& guessedState,
const CommandListSPtr& realAvailableCommands) const -> CommandChoiceResults;
const vector<CommandProto>& realAvailableCommands) const -> CommandChoiceResults;
[[nodiscard]] auto ChooseCommandIndex(
const GameSettingsSPtr& settings,
const net::eagle0::shardok::api::GameStateView& gsv,
const vector<CommandProto>& realAvailableCommands) const -> CommandChoiceResults;
public:
explicit ShardokAIClient(
PlayerId playerId,
bool isDefender,
const HexMap* hexMap,
const SettingsGetter& settings,
AIAlgorithmType aiAlgorithmType,
ScoringCalculatorType scoringCalculatorType,
const mcts::MCTSConfig& mctsConfig);
const SettingsGetter& settings);
~ShardokAIClient() = default;
[[nodiscard]] auto GetPlayerId() const -> PlayerId { return playerId; }
[[nodiscard]] auto ChooseCommandIndex(const ShardokEngine& engine) const
-> CommandChoiceResults;
// Overload that works on copies of state - allows caller to release lock during AI thinking
[[nodiscard]] auto ChooseCommandIndex(
const GameSettingsSPtr& settings,
const net::eagle0::shardok::api::GameStateView& gsv,
const CommandListSPtr& realAvailableCommands) const -> CommandChoiceResults;
// MCTS configuration methods (only relevant when using MCTS algorithm)
[[nodiscard]] auto GetMCTSConfig() const -> const mcts::MCTSConfig& { return mctsConfig; }
void SetMCTSConfig(const mcts::MCTSConfig& config) { mctsConfig = config; }
};
} // namespace shardok

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