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ChineseChess-AlphaZero is a project that implements the AlphaZero algorithm for the game of Chinese Chess (Xiangqi). It adapts DeepMind’s AlphaZero method—combining neural networks and Monte Carlo Tree Search (MCTS)—to learn and play Chinese Chess without prior human data. The system includes self-play, training, and evaluation pipelines tailored to Xiangqi's unique game mechanics.
Features
- Full AlphaZero framework adapted to Chinese Chess
- Implements neural network architecture for board evaluation and policy prediction
- Uses MCTS for decision-making during gameplay
- Provides self-play for generating training data
- Includes evaluation system to assess trained agents
- Customizable training parameters for optimization
Project Samples
Categories
Reinforcement Learning LibrariesLicense
GNU General Public License version 3.0 (GPLv3)Follow CCZero (中国象棋Zero)
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