
Architecture	Conv stem -> 6 residual blocks x 96 filters -> policy head (7 logits, illegal columns masked) + value head (tanh)
Input	3x6x7 planes: own stones / opponent stones / side-to-move
Parameters	~1,102,248
Format	`model.safetensors` + `config.json` (rebuild with `az.model.create_model`)

Training

Self-play with the latest network: batched MCTS (lockstep games, one GPU forward per tick), Dirichlet root noise (alpha=1.0, eps=0.25), tau=1 sampling for the first 10 plies.
Replay buffer with horizontal-flip augmentation at sample time; loss = soft-target cross-entropy + value MSE, AdamW (lr 1e-3, wd 1e-4).
Gating: a candidate replaces the published best only on >55% score over arena games vs. the current best.
150 iterations, 19,200 self-play games, 7.9 h wallclock.

Evaluation (final iteration, real numbers)

Elo anchored at random agent = 0; pure-MCTS-200 anchor calibrated by ladder matches at 989 Elo.

metric	value
Elo (MLE vs anchors)	1626
vs random agent (40 games)	100%
raw policy (no search) vs random	100%
vs pure MCTS, 200 sims	100%

Usage

python

from az.model import create_model  # az package: see the Space's bundled copy
import json, torch
from safetensors.torch import load_file

model = create_model(json.load(open("config.json")))
model.load_state_dict(load_file("model.safetensors"))

Limitations

Connect Four is solved (first player wins with perfect play); this agent is strong but not perfect — deep tactics beyond its search budget can beat it.
Trained only for the standard 6x7 board.
The 40-game gating/eval matches carry ~8% sampling noise per point; the Elo curve's fine structure is noisy even though the trend is real.

Source repository

程式碼、訓練與評估流程、測試與完整證據都在 GitHub：https://github.com/kuotunyu/alphazero-connect4。GitHub kuotunyu 與 Hugging Face steven0226 為同一人。

Source code, the training/evaluation pipeline, tests and the full evidence trail live at https://github.com/kuotunyu/alphazero-connect4. GitHub kuotunyu and Hugging Face steven0226 are the same author.

steven0226/alphazero-connect4

AlphaZero-style Connect Four agent

Model

Training

Evaluation (final iteration, real numbers)

Usage

Limitations

Source repository

steven0226/
alphazero-connect4