AlphaGo Zero Implementation Using Reinforcement Learning.

This is my implementation of the DeepMind's AlphaZero algorithm for the Game of Go. For reference, Mastering the game of Go without human knowledge

Our source code consists of the following files inside the utils_6 folder :

• policyValueNet.py : Architecture and training procedure for the neural network
• MCTS.py : Gives policy for a given state after running MCTS for 100 simulations.
• selfPlay.py : Starts training the model after loading the latest checkpoint
• utils.py : Contains helper functions to be used for MCTS & Policy-Value Network
• config.py : Parameters required for the configuration
• enums.py : Enumeration class for BLACK & WHITE colours

To install the dependencies of this project, run: pip install -r requirements.txt

To download the model in model_6 folder: sh download_model_6.sh

To start the train (best checkpoint will be automatically loaded): sh train.sh

To start playing with AlphaGoPlayer_6.py against a random/fixed agent, execute: python tournament.py

To visualise the decrement in the loss of policy & value (inside the utils_6 folder): tensorboard logdirs='./logs' --port=6006

Video Presentation of Project:

https://youtu.be/QFFr5hk9AMY

Report:

https://drive.google.com/file/d/1ySijuY1zqv6LzkQSo0Fgf7zP9mCWvexQ/view?usp=sharing