Arcade-Learning-Environment: An Evaluation Platform for General Agents

The Arcade Learning Environment (ALE) -- a platform for AI research.

This is the 0.5 release of the Arcade Learning Environment (ALE), a platform designed for AI research. ALE is based on Stella, an Atari 2600 VCS emulator. More information and ALE-related publications can be found at

http://www.arcadelearningenvironment.org

We encourage you to use the Arcade Learning Environment in your research. In return, we would appreciate if you cited ALE in publications that rely on it (BibTeX entry at the end of this document).

Feedback and suggestions are welcome and may be addressed to any active member of the ALE team.

Enjoy, The ALE team

=============================== Quick start

Install main dependences:

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

Compilation:

$ mkdir build && cd build
$ cmake -DUSE_SDL=ON -DUSE_RLGLUE=OFF -DBUILD_EXAMPLES=ON ..
$ make -j 4

To install python module:

$ pip install .
or
$ pip install --user .

Getting the ALE to work on Visual Studio requires a bit of extra wrangling. You may wish to use IslandMan93's Visual Studio port of the ALE.

For more details and installation instructions, see the website and manual. To ask questions and discuss, please join the ALE-users group.

=============================== 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 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)

===================================== Citing The Arcade Learning Environment

If you use ALE in your research, we ask that you please cite the following.

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",
}