Instructions

Paper can be found at: https://arxiv.org/abs/2207.05991

Dependencies required:

• python 3.0
• tensorflow
• numpy
• copy

Acknowledgements:

• AlphaZero code was adapted from https://adsp.ai/blog/how-to-build-your-own-alphazero-ai-using-python-and-keras/

File Descriptions:

• main.py: runs the BTTT tournament.
• gym.py: is the code for the BTTT environment, plus the in-built Minimax, MCTS and Random agents.
• time.py: runs the code to determine the runtime of each agent.
• alphazero.py: contains the code for the AlphaZero agent. Change the MODEL_NAME to the .h5 file which contains the trained model's weights. We provide the baseline AlphaZero Baseline.h5 trained only on starting brick position D4, AlphaZero R2.h5 trained on starting brick positions randomly selected between D3 and D4, AlphaZero R3.h5 trained on starting brick positions randomly selected between C3, D3 and D4.
• config.py: Contains the hyperparameters of the AlphaZero model. To select AlphaZero NS, AlphaZero 100 and AlphaZero 1000, set MCTS_SIMS to 0, 100, 1000 respectively. Make sure DIRICHLET is set to False during the testing phase to let the model play optimally.
• Tutorial.ipynb: a tutorial on how to use the BTTT environment.
• own_agent.py: contains the code for your own agent. (Default: Random)
• Other files and run folder: used to train AlphaZero agent

BTTT Tournament Setup Instructions:

• To run the tournament, type python main.py into a command line interface
• Follow the prompts to select Player 1, Player 2, brick starting location, as well as the number of games
• To edit the version of AlphaZero you want to do it, edit the hyperparameters in config.py

Runtime Analysis Instructions:

• To do runtime analysis for each agent, type python time.py into a command line interface

AlphaZero training instructions:

• To train AlphaZero, open alphazero.ipynb and run the cells
• Hyperparameters can be edited in config.py. Important: Set DIRICHLET variable to True when training, and False when testing. This controls the amount of exploration in the first TURNS_UNTIL_TAU0 steps through adding of dirichlet noise.
• Training environments (Variant 1 or initial brick random between a few cells) can be changed in game.py
• Initial configurations can be edited in initialize.py
• Folder options can be edited in settings.py. Default model and memory storage folder is ./run