The Arcade Learning Environment

The Arcade Learning Environment (ALE) is a simple object-oriented framework that allows researchers and hobbyists to develop AI agents for Atari 2600 games. It is built on top of the Atari 2600 emulator Stella and separates the details of emulation from agent design. This video depicts over 50 games currently supported in the ALE.

This patch is just an unofficial hard fork. The original code can be found here.

What is this patch for?

• I had a hard time installing ALE on Windows 10 and Mac OS. Moreover, the Visual Studio port of IslandMan93 uses outdated code ALE 0.0.1. Therefore, I created this multi-platform patch for ALE 0.6.0.
• This patch supports latest Mac OS Sierra and Windows 10 (with ALE 0.6.0).
• User can set configuration file for each environment using function ale.setString(b'config', b'path_to_config_file').
• Merge IslandMan93's Visual Studio 2013 port of the ALE 0.0.1 to ALE 0.6.0.
• This patch is indexed as version 0.6.1.
• Installation guide in more details.
• Please let me (garlicdevs@gmail.com) know if you cannot install this patch.

Installation Guide

Mac OS

• I successfully installed ALE using the following steps on Mac OS 10.12 and Python 3.6.3

1. Install brew:

 /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

2. Install dependences:

brew install sdl sdl_image sdl_mixer sdl_ttf smpeg portmidi cmake

3. Install Arcade Learning Environment:

git clone https://github.com/garlicdevs/Arcade-Learning-Environment.git
cd Arcade-Learning-Environment
mkdir build && cd build  
cmake -DUSE_SDL=ON -DUSE_RLGLUE=OFF -DBUILD_EXAMPLES=ON -DMAC_OS=ON ..
make -j 4
cd ..
env MACOSX_DEPLOYMENT_TARGET=10.9 pip3.6 install .

Linux

1. Install dependences:

sudo apt-get install libsdl1.2-dev libsdl-gfx1.2-dev libsdl-image1.2-dev cmake

2. Install Arcade Learning Environment:

git clone https://github.com/garlicdevs/Arcade-Learning-Environment.git
cd Arcade-Learning-Environment
mkdir build && cd build  
cmake -DUSE_SDL=ON -DUSE_RLGLUE=OFF -DBUILD_EXAMPLES=ON ..
make -j 4
cd ..
pip3.6 install .

Windows

• I successfully installed ALE on Windows 10 (Visual Studio 2017) and Python 3.6.2 using the following steps:

1. Download project

git clone https://github.com/garlicdevs/Arcade-Learning-Environment.git

2. Open Visual Studio 2017 -> Open solution ALE.sln in folder visual_studio -> Set configuration (Release|x64) -> build ALE -> build ale_python_interface If you build projects successfully, the library ale_python_interface.dll will appear in folder visual_studio\x64\Release\

3. Install Arcade Learning Environment:

cd Arcade-Learning-Environment
python winsetup.py install

List of command-line parameters

Execute ./ale -help for more details; alternatively, see documentation available at http://www.arcadelearningenvironment.org.

-random_seed [n] -- sets the random seed; defaults to the current time

-game_controller [fifo|fifo_named] -- specifies how agents interact
  with the ALE; see Java agent documentation for details

-config [file] -- specifies a configuration file, from which additional 
  parameters are read

-run_length_encoding [false|true] -- determine whether run-length encoding is
  used to send data over pipes; irrelevant when an internal agent is 
  being used

-max_num_frames_per_episode [n] -- sets the maximum number of frames per
  episode. Once this number is reached, a new episode will start. Currently
  implemented for all agents when using pipes (fifo/fifo_named) 

-max_num_frames [n] -- sets the maximum number of frames (independent of how 
  many episodes are played)

Support the authors of The Arcade Learning Environment

By citing the following papers if you use ALE

M. G. Bellemare, Y. Naddaf, J. Veness and M. Bowling. The Arcade Learning Environment: An Evaluation Platform for General Agents, Journal of Artificial Intelligence Research, Volume 47, pages 253-279, 2013.

In BibTeX format:

@Article{bellemare13arcade,
  author = {{Bellemare}, M.~G. and {Naddaf}, Y. and {Veness}, J. and {Bowling}, M.},
  title = {The Arcade Learning Environment: An Evaluation Platform for General Agents},
  journal = {Journal of Artificial Intelligence Research},
  year = "2013",
  month = "jun",
  volume = "47",
  pages = "253--279",
}

If you use the ALE with sticky actions (flag repeat_action_probability), or if you use the different game flavours (mode and difficulty switches):

M. C. Machado, M. G. Bellemare, E. Talvitie, J. Veness, M. J. Hausknecht, M. Bowling. Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents, CoRR abs/1709.06009, 2017.

In BibTex format:

@Article{machado17arcade,
  author = {Marlos C. Machado and Marc G. Bellemare and Erik Talvitie and Joel Veness and Matthew J. Hausknecht and Michael Bowling},
  title = {Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents},
  journal = {CoRR},
  volume = {abs/1709.06009},
  year = {2017}
}