Breakout-DQN

This project uses the Tensorflow framework to implement a Deep Q-Learning Network for the popular Atari game, Breakout.

Model details:

• 2 convolutional layers (32 filters, 4 strides and 64 filters, 2 strides)
• 1 dense layer with 512 neurons
• 1 dense output layer, 4 actions
• Adam optimizer, 5e-4 learning rate
• Huber loss function

This is the best model I have tried, but the convergence is still slow. It reaches some level of intelligent play (or mean reward 4) after ~300.000 frames. Best observed mean reward: 18 after ~4.000.000 frames.

Usage:

• run trainer.py for training a new model or continue training
• run player.py for testing the trained models

Further improvement:

• Limit frames per episode, some games take way longer to play than others.
• Better model