PPO on minichess

This is a class project for AA228 at Stanford. We aim to train an agent using Proximal Policy Optimization on ray.rllib's distributed computing infrastructure to play Gardner minichess.

Contents

Contents
Navigation
Rules
References

Navigation

.
├── games                   # Implementation of minichess variations' board and game logic
├── mcts_old                # Initial experiments with mcts (hence the repo name)
├── mcts_for_train.yml      # Up-to-date conda requirements for macos
├── sample_games.txt        # Sample games from log output
├── train.py                # Main training script

Rules

Many variations of Gardner minichess exist. We preserve most well-known chess concepts (e.g. castling, en-passant, fifty-move), but for purposes of incremental development of our agent, we may add in these rules one by one. Following the implementation of meta-minichess, we define game termination as when either side's king is captured (including by the opposing king too).

References

Meta-learning experiments for the game of minichess and related rule variants. - Michael Hiebert (code) Proximal Policy Optimization Algorithms - John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov (paper)

shiningsunnyday/mcts-chess

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