yogeshwaran13/chess-alphazero-openenv

♟️ Chess AlphaZero — OpenEnv

AlphaZero-style chess agent using MCTS + ResNet self-play.

Architecture

• —ResNet: 5 residual blocks, 64 channels
• —Input : 19 × 8 × 8 board tensor
• —Output: Policy (4096 moves) + Value (-1 to +1)

Training Results

• —Final Policy Loss : 1.4836
• —Final Value Loss : 0.0030
• —Avg Game Length : 34.0 moves
• —Total Iterations : 20

Usage

import torch
net = ChessNet(in_channels=19, channels=64, num_res_blocks=5)
ckpt = torch.load("chess_alphazero_final.pt")
net.load_state_dict(ckpt["model"])
net.eval()