https://github.com/babayara/deepmind-atari-deep-q-learner

The original code from the DeepMind article + my tweaks

https://github.com/babayara/deepmind-atari-deep-q-learner

Repository

The original code from the DeepMind article + my tweaks

https://github.com/BabaYara/DeepMind-Atari-Deep-Q-Learner/blob/master/

# DeepMind Atari Deep Q Learner
This repository hosts the [original code](https://sites.google.com/a/deepmind.com/dqn/) published along with [the article](http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html) in Nature and my experiments (if any) with it.

Disclaimer
----------
This implementation is rather old and there are far more efficient algorithms for reinforcement learning available. If you are interested in applying RL to your problem have a look at [Keras-RL](https://github.com/matthiasplappert/keras-rl) or [rllab](https://github.com/openai/rllab) instead.

DQN 3.0
-------

Tested on Ubuntu 14.04 with nVidia GTX 970:  
![alt text](https://raw.githubusercontent.com/kuz/DeepMind-Atari-Deep-Q-Learner/master/gifs/breakout.gif "Playing Breakout")  
More videos on [YouTube Playlist: Deepmind DQN Playing](https://www.youtube.com/playlist?list=PLgOp827qARy0qNyZq5Y6S6vRJO3tb1WcW)

This project contains the source code of DQN 3.0, a Lua-based deep reinforcement
learning architecture, necessary to reproduce the experiments
described in the paper "Human-level control through deep reinforcement
learning", Nature 518, 529533 (26 February 2015) doi:10.1038/nature14236.

To replicate the experiment results, a number of dependencies need to be
installed, namely:
* LuaJIT and Torch 7.0
* nngraph
* Xitari (fork of the Arcade Learning Environment (Bellemare et al., 2013))
* AleWrap (a lua interface to Xitari)
An install script for these dependencies is provided.

Two run scripts are provided: run_cpu and run_gpu. As the names imply,
the former trains the DQN network using regular CPUs, while the latter uses
GPUs (CUDA), which typically results in a significant speed-up.

Installation instructions
-------------------------

The installation requires Linux with apt-get.

Note: In order to run the GPU version of DQN, you should additionally have the
NVIDIA CUDA (version 5.5 or later) toolkit installed prior to the Torch
installation below.
This can be downloaded from https://developer.nvidia.com/cuda-toolkit
and installation instructions can be found in
http://docs.nvidia.com/cuda/cuda-getting-started-guide-for-linux


To train DQN on Atari games, the following components must be installed:
* LuaJIT and Torch 7.0
* nngraph
* Xitari
* AleWrap

To install all of the above in a subdirectory called 'torch', it should be enough to run

    ./install_dependencies.sh

from the base directory of the package.


Note: The above install script will install the following packages via apt-get:
build-essential, gcc, g++, cmake, curl, libreadline-dev, git-core, libjpeg-dev,
libpng-dev, ncurses-dev, imagemagick, unzip

Training DQN on Atari games
---------------------------

Prior to running DQN on a game, you should copy its ROM in the 'roms' subdirectory.
It should then be sufficient to run the script

    ./run_cpu 

Or, if GPU support is enabled,

    ./run_gpu 


Note: On a system with more than one GPU, DQN training can be launched on a
specified GPU by setting the environment variable GPU_ID, e.g. by

    GPU_ID=2 ./run_gpu 

If GPU_ID is not specified, the first available GPU (ID 0) will be used by default.

Storing a .gif for a trained network
------------------------------------

Once you have a snapshot of a network you can run

	./test_gpu  

to make it play one game and store the .gif under `gifs`. For example

	./test_gpu breakout DQN3_0_1_breakout_FULL_Y.t7

Options
-------

Options to DQN are set within run_cpu (respectively, run_gpu). You may,
for example, want to change the frequency at which information is output 
to stdout by setting 'prog_freq' to a different value.

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