chunjiongzhang / atari-agents

Tensorflow implementation of DQN for atari games

atari-agent

Tensorflow implementation of DQN

This implementation contains:

DQN

1. Deep Q-network and Q-learning
2. Experience replay memory
   • to reduce the correlations between consecutive updates
3. Network for Q-learning targets are fixed for intervals
   • to reduce the correlations between target and predicted Q-values
4. Double DQN
5. Dueling DQN

Requirements

• Python 2.7 or Python 3.3+
• gym
• tqdm
• SciPy or OpenCV2
• TensorFlow

Usage

First, install prerequisites with:

$ pip install tqdm gym[all]

To train a model for Breakout:

$ python main.py

or

// Disable game window. Be able to improve training effect with GPU.
$ python main.py --render=False

Results

GPU: GTX 1060 3G

TBD

References

• DQN-tensorflow
• simple_dqn
• Code for Human-level control through deep reinforcement learning

License

MIT License.