This repository contains the code and raw data for my master's thesis Deep reinforcement learning using Monte-Carlo tree search for Hex and Othello.

Due to storage restrictions the trained models from experiment 1 are not in the repository, but can instead be found in OneDrive. Note that only the final models are included, not checkpoints. The models are located in subdirectories of deep_mcst/<game>/saves as files on the form anet-<n>.tar, where n denotes the number of iterations it has been trained for. They are stored as pickled dictionaries of parameters. They can be loaded using the from_path_full method of GameNet subclasses. Many of the training parameters are also included in a parameters.json file in each subdirectory. The post-training evaluations are found as CSV files in deep_mcts/<game>/training in the repository. The format is specified by the header.

The evaluations are found as JSON files in deep_mcts/<game>/simple_rollouts, one for each model. The JSON describes a 6x6x3x2 array with the dimensions corresponding to:

1. Each rollout probability
2. Each rollout probability it was compared to
3. Wins, draws and losses for the rollout probability in the first dimension
4. As the first player, as the second player

The evaluations are found as JSON files in the two subdirectories of deep_mcts/<game>/complex_rollouts. There is one subdirectory for evaluations with a state evaluator and one without, and one file for each model in each folder. The JSON describes an object, where the "results" key corresponds to a 3x2 array with the dimensions corresponding to:

1. Wins, draws and losses for the policy network rollouts
2. As the first player, as the second player

Additionally there are "complex_simulations" and "simple_simulations" keys, corresponding to the number of simulations with and without expansion in each move of each game for policy network rollouts and random rollouts respectively.

If using Poetry, run poetry install. If not, run pip install -r requirements.txt.

If running on a machine with only one GPU, set both train_device and self_play_device in TrainingConfiguration in deep_mcts/train.py to cuda:0.

1. python -m deep_mcts.<game>.train
2. python -m deep_mcts.<game>.evaluate_training

1. python -m deep_mcts.<game>.evaluate_simple_rollouts

1. python -m deep_mcts.<game>.evaluate_complex_rollouts