Zeta36/chess-alpha-zero
An open-source implementation of AlphaGo Zero methods for chess reinforcement learning.
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This project replicates DeepMind’s AlphaGo Zero and AlphaZero approaches to train a chess-playing agent using deep reinforcement learning. It uses a neural network trained via self-play combined with Monte Carlo Tree Search for move selection. The implementation uses Keras and TensorFlow as the underlying deep learning frameworks.
Frequently asked
- What is Zeta36/chess-alpha-zero?
- An open-source implementation of AlphaGo Zero methods for chess reinforcement learning.
- Is chess-alpha-zero open source?
- Yes — Zeta36/chess-alpha-zero is open source, released under the MIT license.
- What language is chess-alpha-zero written in?
- Zeta36/chess-alpha-zero is primarily written in Jupyter Notebook.
- How popular is chess-alpha-zero?
- Zeta36/chess-alpha-zero has 2.2k stars on GitHub.
- Where can I find chess-alpha-zero?
- Zeta36/chess-alpha-zero is on GitHub at https://github.com/Zeta36/chess-alpha-zero.