This project is a demonstration of reinforcement learning techniques applied to a simple problem. The goal of the project is to train an agent to play a 5x5 grid Little Go game and defeat a random player.
The project includes the following files:
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GoGame.py: This is the main file that runs the simulation and trains the agent.- Initializes Q-Learning Agent and Random Player.
- Simulates "battle" with alternate player turns and calculating score.
- Calculates winning rate of the agent.
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GoAgent.py: This file contains the implementation of the agent and its Q-Learning Algorithm.- Initializes Q-values for initial board state.
- Contains implementation of player moves and Q-learning.
- Finds out best possible move to play.
- Loads and Saves Q-values in the Qval.txt.
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GoBoard.py: This file contains the implementation of Little-Go environment.- Visualizing and resetting the board.
- Finding neighbors and liberty of the pieces.
- Removing dead pieces form the board.
- Calculating scores for the players and checking winner.
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GoRandom.py: This file contains the implementation of a random player. -
Qval.txt: This file is generated after the agent has learnt and contains Q-Values for each state.
The project is intended for educational and research purposes.
We hope you enjoy the project and learn something new about reinforcement learning!