This project tackles the training of a DQN (Deep Q-Network) agent to learn how to play the popular Atari game "Breakout".

the training was done on a 8cpu GCP VM, and it took a whole day to train the agent for 1M iteration.

The agent reached a superhuman level (meaning it never loose) by just trail and error and using only the game image as an input (Like any human player).

The Architecture and hyperparams of the DQN network are the same used in the original DQN paper by Deepmind https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf

the trained agent in action :

lot of this work is inspired by Auélien Géron's Book "Hands-On Machine learning with Scikit-Learn, Keras & TensorFlow" https://github.com/ageron/handson-ml2/

create a conda environment using the requirement.txt file:

conda create --name rl_env --file requirement.txt

and you are all set !

This uses Tensorflow 2.0 and TF-agents 0.3. Workes and tested on Window10 and Linux as well

• Tensorflow
• TF-agents
• OpenAI's Gym[atari] environment