Mandala RL

Self-play reinforcement learning system for training a strong Mandala bot using MCTS + neural networks (AlphaZero-style).

Overview

This project trains a Mandala bot through 100% self-play using:

• Monte Carlo Tree Search (MCTS) for game tree exploration
• Policy/Value neural network for position evaluation
• Self-play data generation
• Iterative training and evaluation
• Deterministic Elo-based evaluation ladder

Optimized for minimal GPU compute on Apple Silicon (MPS backend).

Quick Start

# Install dependencies
pip install -r requirements.txt

# Train the bot
python scripts/train.py --config configs/default.yaml

# Evaluate against previous versions
python scripts/evaluate.py --checkpoint data/checkpoints/model_latest.pt

Project Structure

mandala_rl/
├── game/          # Mandala game engine and rules
├── mcts/          # Monte Carlo Tree Search implementation
├── network/       # Policy/Value neural network
├── selfplay/      # Self-play game generation
├── training/      # Training loop and replay buffer
└── evaluation/    # Elo rating and arena evaluation

Requirements

• Python 3.10+
• PyTorch 2.0+ with MPS support
• Apple Silicon Mac (M1/M2/M3)