Checkmating One, by Using Many

The release contains the model files of our accepted paper: Checkmating One, by Using Many: Combining Mixture of Experts With MCTS to Improve in Chess at IEEE Transactions on Games:

• https://ieeexplore.ieee.org/document/11372147

Model information:

• big_3x_risev33_after_rl.zip: Large risev33 network trained on KingBaseLight and later improved via RL for 10 model updates (newest strongest model).
• big_3x_risev33.zip: Large risev33 network trained on KingBaseLight dataset.
• checkmating_one_by_using_many_models.zip: Singular models trained via separated learning, weighted learning, and staged learning.
• Pommerman_MoE_Models.zip: Includes the mixture of experts models for Pommerman.

All models, except the Pommerman ones, are only usable for chess.

The repository for our Mixture of Experts (MoE) experiments for Pommerman can be found here:

• https://github.com/axeis/PommerLearn/tree/phases

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