This project is to use Machine Learning techniques, such as CNN, Supervised Learning and Reinforcement Learning, to build and train a Game AI for Chinese Chess. Training data were collected via internet. Tensorflow was used to build the neural network models and Aliyun was used for cloud service.
- run the server
cd ./site/Sites
npm start
- run the python program
cd ./code
python server_new.py
if you want to use the evaluator from Eleeye, set the path in the common.py
| File | Function |
|---|---|
| check.py | test check or checkmate |
| chess_new.py | play chess with itself |
| common.py | common functions |
| data_process.py | process data for NN |
| information.py | extract information from pgn |
| information_eva.py | extract data for Evaluation network |
| information_ext.py | extract data for Policy network |
| model_training_move.py | train move selector |
| model_training_piece.py | train piece selector |
| moveGeneration.py | generate possible moves |
| move_selector.py | move selector |
| piece_selector.py | piece selector |
| server_new.py | server program |
| train_eva_full.py | train evaluation network |
| trans.py | process PGN |
| update_move.py | RL for move selector |
| update_piece.py | RL for piece selector |
| validation.py | move validation |
| chess2p\ | 2p chess for RL |