Arcade Learning Environment (ALE) Framework

An Arcade Learning Environment (ALE) Framework is a object-oriented AI agent framework focused on Atari 2600 games.

• See: Atari 2600.

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• https://github.com/mgbellemare/Arcade-Learning-Environment
  • QUOTE: 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. ...

    Features

    • [[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).
    • Python development is supported through ctypes.
    • Agents programmed in C++ have access to all features in the ALE.
    • Visualization tools.

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