This repository achieves three goals:

1. Implements the game of Quoridor (rules).
2. Uses self-play deep Q-Learning to train an AI agent to play the game.
3. Let's users pause training and play against the AI at any point.

When the constant DISPLAY_GAME is True, then the pygame window will open and the following commands will be available:

• d - Toggle redraws of the screen (drawing slows down learning)
• h - Jump into the game and start playing!
• f - Change the game speed
• r - Toggle full inference mode (100% prediction, no random moves for exploration)

• main.py - Runs a finite number of games. The AI learns by playing itself over and over.
• game.py - Has run() which is the game loop. Every turn is characterized by an agent evaluating the state, that agent making a move and the state being updated accordingly.
• display_game.py Displays the game by mapping the state onto a graphical representation.
• actions.py All the actions that an agent can take.
• state.py The state of the game. This checks if actions are legal and converts between the global state and the agent's perspective of the state.
• model.py This is the Q-learning neural network that makes action predictions and updates depending on the reward feedback.
• agents.py This consists of an Agent class and two subclasses, each for the two agents playing. Each agent has a different view of the board so therefore need to convert the state to their perspective.

1. Run python -m venv venv
2. Run venv/Scripts/activate (windows)
3. Run pip install -r requirements.txt
4. Run python main.py
   Note this project uses an older version of Tensorflow (1.14)

MIT License