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deep_q_learning

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This is the Code for "Deep Q Learning - The Math of Intelligence #9" By Siraj Raval on Youtube

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# deep_q_learning
This is the Code for "Deep Q Learning - The Math of Intelligence #9" By Siraj Raval on Youtube

# Coding Challenge - Due Date, Thursday August 17th 2017

This weeks challenge is to use Q learning to train an agent for any game you'd like. You can use [OpenAI's Gym](https://github.com/openai/gym) or [Universe](https://github.com/openai/universe) as a simulation testbed, but for the Q-Learning algorithm itself don't use any libraries. Bonus points if you use a deep convolutional network from scratch as well to learn from pixels, which means your agent is generalized to more than just one game (Deep Q Learning). Good luck!

## Overview

This is the code for [this](https://youtu.be/79pmNdyxEGo) video on Youtube by Siraj Raval as part of the Math of Intelligence series. We're going to rerecreate DeepMind's Deep Q Learner for a variety of games.

## Dependencies

* keras (http://www.pyimagesearch.com/2016/11/14/installing-keras-with-tensorflow-backend/)
* tensorflow (https://www.tensorflow.org/install/)
* gym (https://github.com/openai/gym)
* collections

Use [pip](https://pip.pypa.io/en/stable/) to install any dependencies.

## Usage

Just run `jupyter notebook` in terminal and the code will run. If you'd like to run this code on Super Mario, you need to install [this](https://github.com/ppaquette/gym-super-mario) additonal dependency.

## Credit

The credit for this code goes to [PeterWittek](https://github.com/peterwittek/qml-rg/tree/a9972750dc25c3d906dc1c4fa764992317453eb9). I've merely created a wrapper to get people started.

deep_q_learning

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