This repo contains an implementation of this paper in TensorFlow.

Watch it play Breakout here

The code runs and learns, but I'm still testing and changing it. It does very well on Pong and Breakout. The code is still a little messy in some places, but will be cleaned up in the future.

1. An nVidia GPU with GDDR5 memory to train in a reasonable amount of time
2. Python 3
3. The Arcade Learning Environment for the emulator framework.
4. Tensorflow for gpu numerical computions and symbolic differentiation.
5. Linux/OSX, because Tensorflow doesn't support Windows.
6. Matplotlib and Seaborn for visualizations.
7. OpenCV for image scaling. Might switch to SciPy since OpenCV was a pain for me to install.
8. Any dependencies of the above software, of course, like NumPy.

From the top directory of the repo (dir with python files):

$ python3 ./run_dqn.py <name_of_game> <name_of_algorithm/method> <name_of_agent_instance>

For example:

$ python3 ./run_dqn.py breakout dqn brick_hunter

####Watching $ python3 ./run_dqn.py <name_of_game> <name_of_algorithm/method> <name_of_saved_model> --watch Where <name_of_saved_model> is the <name_of_agent_instance> used during training. If you used any non-default settings, make sure to use the same ones when watching as well.

You can change many hyperparameters/settings by entering optional arguments. To get a list of arguments:

$ python3 ./run_dqn.py --h

By default rom files are expected to be in a folder titled 'roms' in the parent directory of the repo. You can pass a diferent directory as an argument or change the default in run_dqn.py.

Statistics and saved models are saved in the parent directory of the repo as well.

A full training run takes between 3.5 and 4 days on my nVidia GTX 970.

The parallel option hasn't been updated recently and does not currently work.