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.
