MicroZero

MicroZero is a tiny AlphaZero-style Tic-Tac-Toe agent. A shared neural trunk predicts both move priors and position value. Monte Carlo Tree Search converts those estimates into stronger self-play targets, while terminal outcomes supervise value learning.

The training loop uses:

• neural-guided PUCT search;
• root Dirichlet exploration;
• policy targets from MCTS visit counts;
• terminal win/draw/loss value targets;
• all eight square-board symmetries;
• a bounded replay buffer.

Evaluation alternates playing first and second against random and exact minimax opponents.

Verified results

• 7,626 trainable parameters;
• 980 neural-MCTS self-play games;
• 24,000-example bounded replay buffer after symmetry augmentation;
• 384 wins, 16 draws, and zero losses over 400 games against random play;
• 200 draws and zero losses over 200 games against exact minimax.

The exact opponent is used only for final evaluation, not as a source of training targets. Full metrics and the per-iteration learning history are stored in artifacts/microzero/evaluation.json.

Reproduce

uv run python projects/microzero/train.py