Playing Atari games with Double Deep Q Learning

This is an implementation of the Double Deep Q Learning algorithm, introduced by researchers from Google DeepMind in 2015. This is an off-policy Reinforcement Learning model, capable of dealing with the overestimation issue of the classic Deep Q Learning model.

Installation

The implementation utilizes OpenAI's Gym library to run the Atari emulation, which can only run properly on a Linux machine. Don't try to run this on Windows :(

Packages this project depends on are listed in the file requirements.txt, and can be install system-wide or in a virtual environment with Pip:

pip3 install -r requirements.txt

Also, GPU support for Tensorflow should also be installed, so that the model can easily run in real time.

How to use

Run python3 main.py with the following parameters:

• -g, --game: Choose from available games. Default is "Breakout"
• -m, --mode: Choose from available modes: training, testing. Default is "training".
• -tsl, --step_per_run_limit: Choose how many total steps (frames visible by agent) should be performed. Default is 10000.
• -trl, --total_run_limit: Choose after how many runs we should stop. Default is None (no limit).
• -r, --render: Choose if the game should be rendered. Default is False.
• -s, --sign_only: Choose whether we should clip rewards to its sign. Default is True.