Checkmating One, by Using Many

Instructions for reproducing the results in the paper

Game phase extraction for training in game_phase_detector.py. Game phase extraction for MCTS in the board.cpp get_phase function. Change first if condition to (num_phases == 3 && true) to use lichess phase definition when using three experts.

Workflow of training one phase expert network:

1. Gather dataset pgn files and specify default_dir in main_config.py. The pgn files should be placed in <default_dir>/pgn/<set_type>, see main_config.py for more details.
2. Specify phase and phase_definition (either "lichess" or "movecountX") in main_config.py
3. Generate planes (input representation) from pgn files with convert_pgn_to_planes.ipynb
4. Use train_cnn.py to train the expert
   • tc.seed=9 was used for all our experiments
   • Specify tc.export_dir for the model export directory
   • Adjust phase_weights as needed. Use 1.0 for equal weights for all phases.
   • For the weighted learning approach, set phase in main_config.py to None and only adjust phase_weights in train_cnn.py
5. Repeat process for each expert

Workflow to use phase experts as an MoE agent in MCTS:

1. Copy .onnx and .tar files from the best-model folder of each expert training export directory
2. Paste .onnx and .tar files from expert i to a model directory, e.g. /data/model/ClassicAra/chess/<moe_name>/phase<i> (one phase<i> folder for each phase/expert)
3. Build ClassicAra binary, see Build Instructions
4. Launch the ClassicAra binary.
   • Specify the model directory as needed, e.g. "setoption name Model_Directory value /data/model/ClassicAra/chess/correct_phases"
   • Specify Batch_Size as needed, e.g. "setoption name Batch_Size value 8"
   • Specify GPU as needed, e.g. "setoption name First_Device_ID value 0"
   • Generate .trt files by executing the "isready" command
5. Run Cutechess match to compare different approaches, see run_cutechess_experiments.py for exemplary cutechess commands