Zeta36/chess-alpha-zero resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2224 stars · 472 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A research/educational project implementing chess reinforcement learning using AlphaGo Zero methods (self-play, MCTS, residual neural networks) in Python with Keras and TensorFlow. It includes notebooks and a supervised-learning-trained model, though full self-play training is impractical on a single machine.

Use cases

• learn how alphago zero works by studying code
• train a neural network to play chess
• implement mcts with a policy-value network
• run a chess ai demo in a jupyter notebook
• experiment with self-play reinforcement learning
• reproduce deepmind's alphazero approach on chess

When to choose

• you want to study or learn AlphaZero-style RL algorithms hands-on
• you want a readable reference implementation in Keras/TensorFlow
• you want to experiment with MCTS plus neural networks on chess

When to avoid

• you need a strong, production-ready chess engine (use Stockfish or Leela Chess Zero)
• you want to complete full self-play training on a single machine (too computationally expensive)
• you need actively maintained, up-to-date dependencies (the project is largely historical)

Facets

learning-resource · maturity maintenance

reinforcement-learning machine-learning deep-learning reinforcement-learning machine-learning python cross-platform alphago-zero chess mcts keras tensorflow self-play jupyter-notebooks game-development gpu

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markdown · JSON · MCP: product_card(name="Zeta36/chess-alpha-zero")