##Overview This is the code for the SF Python meetup group tutorial on reinforcement learning. We will build the game of Pong using Pygame and then build a Deep Q Network using OpenCV and Tensorflow. Then we will train the network to play the game. The DQN is a convolutional neural network that reads in pixel data from the game and the game score.

##Installation

1. Install Continuum miniconda (https://conda.io/miniconda.html)
2. Run conda env create
3. Run source activate pong

This should install all necessary dependencies in a painless way.

For manual install, here are the required dependencies and links to install them:

• python=2.7
• numpy>=1.7
• opencv>=2.4.8 (http://www.pyimagesearch.com/2015/06/15/install-opencv-3-0-and-python-2-7-on-osx/)
• pygame>=1.9.3 (https://www.pygame.org/download.shtml)
• tensorflow>=1.0.0 (https://www.tensorflow.org/install/)

##Usage Once you've completed the exercises, you can run it like in terminal:

python RL.py

The longer you let it run, the better it will get.

##Solutions Solution code is provided in the solutions folder.

##Credits

Code originally developed by malreddysid, updated by llSourcell. I've adapted it to TF 1.0, Anaconda python and adapted to be used for an exercise.

##References

• http://karpathy.github.io/2016/05/31/rl/
• http://rll.berkeley.edu/deeprlcourse/
• http://www.wildml.com/2016/10/learning-reinforcement-learning/
• https://www.nervanasys.com/demystifying-deep-reinforcement-learning/