suragnair/alpha-zero-general

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

4506 stars · 1153 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A clean, flexible implementation of the AlphaZero self-play reinforcement learning algorithm that can be adapted to any two-player turn-based game and any deep learning framework. It includes implementations for Othello, GoBang, TicTacToe, and Connect4, plus an accompanying tutorial.

Use cases

• train an AlphaZero agent for a custom board game
• learn how self-play reinforcement learning and MCTS work
• reproduce AlphaGo Zero-style training on Othello or Gomoku
• compare neural network frameworks like PyTorch and Keras for RL
• play against a pretrained AlphaZero Othello model
• implement game logic and neural nets for a new two-player game

When to choose

• you want a readable, educational AlphaZero codebase to extend to your own game
• you need a reference implementation of MCTS with self-play in Python
• you are doing a course project or research prototype on game-playing AI

When to avoid

• you need a highly optimized, distributed AlphaZero implementation for large games like full Go or chess
• you want production-ready training infrastructure with asynchronous self-play
• you need prebuilt agents for commercial games rather than a framework to train your own

Facets

library · maturity maintenance

reinforcement-learning machine-learning deep-learning game-engine simulation reinforcement-learning machine-learning tutorials python cross-platform alpha-zero mcts self-play alphago-zero board-games othello gomoku pytorch keras tensorflow game-development gpu docker

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markdown · JSON · MCP: product_card(name="suragnair/alpha-zero-general")