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.

For an overview of our goals for the 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. See the Citing section for BibTeX entries.

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 macOS, Windows, and several Linux distributions).
• Python development is supported through pybind11.
• Agents programmed in C++ have access to all features in the ALE.
• Visualization tools.

Quick Start

You must have a valid C++17 compiler and the following dependencies installed (we recommend using vcpkg on all platforms)

vcpkg install zlib sdl1

Note: sdl is optional but can be useful for display/audio support (i.e., display_screen and sound config options).

Python

The package ale-py will be distributed via PyPi but for the time being Python users can install the ALE via

pip install .

Note: Make sure you're using an up to date version of pip or the install may fail.

You can now import the ALE in your Python projects with

from ale_py import ALEInterface

ale = ALEInterface()
ale.loadROM(...)

C++

We use CMake as a first class citizen and you can use the ALE directly with any CMake project. To compile and install the ALE you can run

mkdir build && cd build
cmake ../ -DCMAKE_BUILD_TYPE=Release
cmake --build . --target install

There are optional flags -DSDL_SUPPORT=ON/OFF to toggle SDL support (OFF by default), -DBUILD_CPP_LIB=ON/OFF to build the ale-lib C++ target (ON by default), and -DBUILD_PYTHON_LIB=ON/OFF to build the pybind11 wrapper (ON by default).

Finally you can link agaisnt the ALE in your own CMake project as follows

find_package(ale REQUIRED)
target_link_libraries(YourTarget ale::ale-lib)

Citing

If you use the 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",
}

If you use the ALE with sticky actions (flag repeat_action_probability), or if you use the different game flavours (mode and difficulty switches), we ask you that you also cite the following:

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, Journal of Artificial Intelligence Research, Volume 61, pages 523-562, 2018.

In BibTex format:

@Article{machado18arcade,
    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 = {Journal of Artificial Intelligence Research},
    volume = {61},
    pages = {523--562},
    year = {2018}
}