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[4/6] Add iterative deepening with aspiration windows and TT move ordering #36
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[4/6] Add iterative deepening with aspiration windows and TT move ordering #36
luccabb
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feature/mvv-lva-killer-moves
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feature/iterative-deepening-aspiration
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…ering Implements iterative deepening for better move ordering and future time management: **Iterative Deepening:** - Search depth 1, then 2, then 3, ... up to target depth - Cache persists across all iterations (TT entries reused) - Killer moves persist across iterations - Best move from depth N-1 is tried first at depth N (via TT) **Aspiration Windows:** - After depth 1, use narrow window (±50 centipawns) around previous score - If search fails outside window, re-search with doubled window - Falls back to full window after 500cp expansion - Reduces nodes searched when score is stable **TT Move Ordering:** - Save best move from TT lookup even if score can't be used - Put TT move first in move list before searching - Significantly improves move ordering at all depths Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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This adds a chess reinforcement learning environment following the OpenEnv interface pattern, with both local and HTTP client-server modes. Features: - ChessEnvironment class with configurable rewards, opponents, and game limits - FastAPI server with REST endpoints (/reset, /step, /state, /engine-move) - HTTP client for remote environment access - Web UI for playing against the engine - HuggingFace Spaces deployment configuration (Dockerfile, openenv.yaml) - Example training scripts for local and remote usage Also includes: - mypy configuration for optional RL dependencies - Import formatting fixes for ufmt compliance
* Add OpenEnv-compatible RL environment with HuggingFace Space This adds a chess reinforcement learning environment following the OpenEnv interface pattern, with both local and HTTP client-server modes. Features: - ChessEnvironment class with configurable rewards, opponents, and game limits - FastAPI server with REST endpoints (/reset, /step, /state, /engine-move) - HTTP client for remote environment access - Web UI for playing against the engine - HuggingFace Spaces deployment configuration (Dockerfile, openenv.yaml) - Example training scripts for local and remote usage Also includes: - mypy configuration for optional RL dependencies - Import formatting fixes for ufmt compliance * Remove Elo claim and fix GitHub link to open in new tab
Fixes: - Remove incorrect `bash .env` line (was trying to execute .env as script) - Add `set -e` to exit on errors - Check if brew is installed before using it - Check if git-lfs/envsubst already installed before reinstalling - Validate build succeeded before continuing - Verify dist/moonfish exists before copying - Check if lichess-bot directory exists - Validate LICHESS_TOKEN is set after sourcing .env - Validate token is not empty when creating .env - Use `cp -f` instead of rm + cp Improvements: - Make lichess-bot directory configurable via LICHESS_BOT_DIR env var - Add progress messages for better UX - Provide helpful error messages with next steps Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
* Add Stockfish benchmark CI workflow - Runs cutechess-cli matches against Stockfish on every PR - 20 rounds with max concurrency - Moonfish: 60s per move, Stockfish: Skill Level 5 with 60+5 time control - Downloads full 170MB opening book from release assets (bypasses LFS) - Reports win/loss/draw stats in GitHub job summary - Uploads PGN and logs as artifacts Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * Parallelize Stockfish benchmark with matrix strategy - Run 20 parallel jobs (10 chunks × 2 skill levels) - Test against both Stockfish skill level 4 and 5 - 100 games per skill level = 200 total games for reliable signal - Add aggregation job to combine results with summary table - Use different random seeds per chunk for opening variety * Add PR comment with benchmark results - Post aggregated results as a comment on the PR - Makes it easy to see win/loss/draw rates without navigating to CI - Includes collapsible configuration details * Add -repeat flag for more consistent benchmark results - Each opening is played twice with colors reversed - Eliminates first-move advantage variance - Doubles games to 400 total (200 per skill level) - More statistically reliable results between runs * Add detailed stats to benchmark PR comment - Show win rates by color (as White / as Black) - Show loss reasons (timeout, checkmate, adjudication) - Separate tables per skill level for clarity * Fix termination parsing and correct game count - Parse game endings from PGN move text (cutechess format) - Track: checkmate, timeout, resignation, stalemate, repetition, 50-move - Fix config: 200 total games (not 400) * Simplify game endings - parse merged PGN directly - Remove per-chunk termination tracking - Parse game endings from merged PGN in aggregate step - Cleaner and less error-prone * Extract game endings dynamically from PGN text * Filter out mates from game endings (redundant with win/loss) * Rename to 'Non-checkmate endings' * Add skill level 3 and skip aggregate if all jobs cancelled - Test against Stockfish skill levels 3, 4, and 5 (300 total games) - Only run aggregate job if at least one benchmark succeeded * Hardcode concurrency to 10 for faster benchmarks * Increase to 20 rounds and 20 concurrency (600 total games) * Reduce to 5 chunks (15 total jobs, 300 games) * Add PR reactions: eyes on start, thumbs up on complete - React with 👀 when benchmark starts - React with 👍 after results are posted * Add local benchmark script * Add skill level 1, increase to 200 games per level (800 total) * Revert CI changes, update local script: skill level 1, 200 games/level * Add skill level 2 to local benchmark script * Update benchmark settings - Local script: 100 rounds, 15 concurrency - CI: Remove eyes reaction when adding thumbs up --------- Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
…eepening-aspiration
* Only run benchmarks when engine code changes * Remove lichess from path filter (not engine code) * Run benchmarks on PRs to any branch, not just master
…eepening-aspiration
🔬 Stockfish Benchmark Resultsvs Stockfish Skill Level 3
vs Stockfish Skill Level 4
Non-checkmate endings:
vs Stockfish Skill Level 5
Non-checkmate endings:
Configuration
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Summary
Details
Iterative Deepening
Instead of searching directly to depth N, we search depth 1, then 2, then 3, etc:
Benefits:
Aspiration Windows
After the first iteration, we use a narrow window around the previous score:
This reduces the number of nodes searched when the score is stable.
TT Move Ordering
Test plan
🤖 Generated with Claude Code