Deep Q-learning the Tic Tac Toe game
This project contains an implementation of the game Tic Tac Toe with several automated agents:
- RandomAgent plays fully random.
- MinMaxAgent plays perfectly. Best expected outcome against this agent therefore is a draw. If the agent has multiple best next agents it chooses one of them randomly. This is actually important to explore multiple paths of the game state.
- DQNAgent is able to learn while playing against any other agent via reinforcment learning. For more information continue reading.
Jupyter Notebook
The results of training can be found in the Jupyter notebook training.ipynb.
DQN Agent
The DQN agent utilizes the features:
- One-hot encoded input layer (3 * 9 cells)
- One-hot encoded output layer (9 actions)
- Single hidden layer with 243 neurons
- Exploration with decaying epsilon value
- Training with replay memory
- Double DQN to stabilize weights
- Dueling DQN
The trained weights can be found in dqnagent-first and dqnagent-second.
The DQN agent has been trained separately as a first and a second player.
To play with the agent without training again you can just load the provided model weights:
agent = DQNAgent(0, is_learning=False)
agent.model = keras.models.load_model('dqnagent-first')
Dependencies
This project in running on Python 3 with the dependencies specified in requirements.txt.