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.

For an overview of our goals for ALE read The Arcade Learning Environment: An Evaluation Platform for General Agents. if you use ALE in your research, we ask that you please cite this paper in reference to the environment [BibTeX]. Also, if you have any questions or comments about the ALE, please contact us through our mailing list.


  • object-oriented framework with support to add agents and games
  • emulation core uncoupled from rendering and sound generation modules for fast emulation with minimal library dependencies
  • automatic extraction of game score and end-of-game signal for more than 50 Atari 2600 games
  • multi-platform code (compiled and tested under OS X and several Linux distributions, with Cygwin support)
  • communication between agents and emulation core can be accomplished through pipes, allowing for cross-language development (sample Java code included)
  • agents programmed in C++ have access to all ALE features
  • visualization tools

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