The Arcade Learning Environment
<a href="#the-arcade-learning-environment">
<img alt="Arcade Learning Environment" align="right" src="docs/static/ale.svg" width=75 />
</a>
===============================
[](https://github.com/mgbellemare/Arcade-Learning-Environment/actions/workflows/ci.yml)
[](https://pypi.org/project/ale-py)
**The Arcade Learning Environment (ALE) is a simple 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](https://stella-emu.github.io) and separates the details of emulation from agent design.
This [video](https://www.youtube.com/watch?v=nzUiEkasXZI) 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](https://jair.org/index.php/jair/article/view/10819).
If you use ALE in your research, we ask that you please cite this paper in reference to the environment. See the [Citing](#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 100
Atari 2600 games.
- Multi-platform code (compiled and tested under macOS, Windows, and several Linux distributions).
- Python bindings through [pybind11](https://github.com/pybind/pybind11).
- Native support for OpenAI Gym.
- Visualization tools.
Quick Start
===========
The ALE currently supports three different interfaces: C++, Python, and OpenAI Gym.
Python
------
You simply need to install the `ale-py` package distributed via PyPI:
```shell
pip install ale-py
```
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
```python
from ale_py import ALEInterface
ale = ALEInterface()
```
### ROM Management
The ALE doesn't distribute ROMs but we do provide a couple tools for managing your ROMs. First is the command line tool `ale-import-roms`. You can simply specify a directory as the first argument to this tool and we'll import all supported ROMs by the ALE.
```shell
ale-import-roms roms/
[SUPPORTED] breakout roms/breakout.bin
[SUPPORTED] freeway roms/freeway.bin
[NOT SUPPORTED] roms/custom.bin
Imported 2/3 ROMs
```
Furthermore, Python packages can expose ROMs for discovery using the special `ale-py.roms` entry point. For more details check out the example [python-rom-package](./examples/python-rom-package).
Once you've imported a supported ROM you can simply import the path from the `ale-py.roms` package and load the ROM in the ALE:
```py
from ale_py.roms import Breakout
ale.loadROM(Breakout)
```
## OpenAI Gym
Gym support is included in `ale-py`. Simply install the Python package using the instructions above. You can also install `gym[atari]` which also installs `ale-py` with Gym.
As of Gym v0.20 and onwards all Atari environments are provided via `ale-py`. We do recommend using the new `v5` environments in the `ALE` namespace:
```py
import gym
env = gym.make('ALE/Breakout-v5')
```
The `v5` environments follow the latest methodology set out in [Revisiting the Arcade Learning Environment by Machado et al.](https://jair.org/index.php/jair/article/view/11182).
The only major change difference from Gym's `AtariEnv` is that we'd recommend not using the `env.render()` method in favour of supplying the `render_mode` keyword argument during environment initialization. The `human` render mode will give you the advantage of: frame perfect rendering, audio support, and proper resolution scaling. For more information check out [docs/gym-interface.md](./docs/gym-interface.md).
For more information on changes to the Atari environments in OpenAI Gym please check out [the following blog post](https://brosa.ca/blog/ale-release-v0.7).
C++
---
The following instructions will assume you have a valid C++17 compiler and [`vcpkg`](https://github.com/microsoft/vcpkg) installed.
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
```sh
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 (i.e., `display_screen` and `sound` 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
```cmake
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:
```bibtex
@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:
```bibtex
@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}
}
```
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"description": "The Arcade Learning Environment\n<a href=\"#the-arcade-learning-environment\">\n <img alt=\"Arcade Learning Environment\" align=\"right\" src=\"docs/static/ale.svg\" width=75 />\n</a>\n===============================\n\n[](https://github.com/mgbellemare/Arcade-Learning-Environment/actions/workflows/ci.yml)\n[](https://pypi.org/project/ale-py)\n\n\n**The Arcade Learning Environment (ALE) is a simple framework that allows researchers and hobbyists to develop AI agents for Atari 2600 games.**\nIt is built on top of the Atari 2600 emulator [Stella](https://stella-emu.github.io) and separates the details of emulation from agent design.\nThis [video](https://www.youtube.com/watch?v=nzUiEkasXZI) depicts over 50 games currently supported in the ALE.\n\nFor an overview of our goals for the ALE read [The Arcade Learning Environment: An Evaluation Platform for General Agents](https://jair.org/index.php/jair/article/view/10819).\nIf you use ALE in your research, we ask that you please cite this paper in reference to the environment. See the [Citing](#Citing) section for BibTeX entries.\n\nFeatures\n--------\n\n- Object-oriented framework with support to add agents and games.\n- Emulation core uncoupled from rendering and sound generation modules for fast\n emulation with minimal library dependencies.\n- Automatic extraction of game score and end-of-game signal for more than 100\n Atari 2600 games.\n- Multi-platform code (compiled and tested under macOS, Windows, and several Linux distributions).\n- Python bindings through [pybind11](https://github.com/pybind/pybind11).\n- Native support for OpenAI Gym.\n- Visualization tools.\n\nQuick Start\n===========\n\nThe ALE currently supports three different interfaces: C++, Python, and OpenAI Gym.\n\n\nPython\n------\n\nYou simply need to install the `ale-py` package distributed via PyPI:\n\n```shell\npip install ale-py\n```\nNote: Make sure you're using an up to date version of `pip` or the install may fail.\n\n\nYou can now import the ALE in your Python projects with\n```python\nfrom ale_py import ALEInterface\n\nale = ALEInterface()\n```\n\n### ROM Management\n\nThe ALE doesn't distribute ROMs but we do provide a couple tools for managing your ROMs. First is the command line tool `ale-import-roms`. You can simply specify a directory as the first argument to this tool and we'll import all supported ROMs by the ALE.\n\n```shell\nale-import-roms roms/\n\n[SUPPORTED] breakout roms/breakout.bin\n[SUPPORTED] freeway roms/freeway.bin\n\n[NOT SUPPORTED] roms/custom.bin\n\nImported 2/3 ROMs\n```\nFurthermore, Python packages can expose ROMs for discovery using the special `ale-py.roms` entry point. For more details check out the example [python-rom-package](./examples/python-rom-package).\n\nOnce you've imported a supported ROM you can simply import the path from the `ale-py.roms` package and load the ROM in the ALE:\n```py\nfrom ale_py.roms import Breakout\n\nale.loadROM(Breakout)\n```\n\n## OpenAI Gym\n\nGym support is included in `ale-py`. Simply install the Python package using the instructions above. You can also install `gym[atari]` which also installs `ale-py` with Gym.\n\nAs of Gym v0.20 and onwards all Atari environments are provided via `ale-py`. We do recommend using the new `v5` environments in the `ALE` namespace:\n\n```py\nimport gym\n\nenv = gym.make('ALE/Breakout-v5')\n```\nThe `v5` environments follow the latest methodology set out in [Revisiting the Arcade Learning Environment by Machado et al.](https://jair.org/index.php/jair/article/view/11182).\n\nThe only major change difference from Gym's `AtariEnv` is that we'd recommend not using the `env.render()` method in favour of supplying the `render_mode` keyword argument during environment initialization. The `human` render mode will give you the advantage of: frame perfect rendering, audio support, and proper resolution scaling. For more information check out [docs/gym-interface.md](./docs/gym-interface.md).\n\nFor more information on changes to the Atari environments in OpenAI Gym please check out [the following blog post](https://brosa.ca/blog/ale-release-v0.7).\n\nC++\n---\n\nThe following instructions will assume you have a valid C++17 compiler and [`vcpkg`](https://github.com/microsoft/vcpkg) installed.\n\nWe use CMake as a first class citizen, and you can use the ALE directly with any CMake project.\nTo compile and install the ALE you can run\n\n```sh\nmkdir build && cd build\ncmake ../ -DCMAKE_BUILD_TYPE=Release\ncmake --build . --target install\n```\n\nThere are optional flags `-DSDL_SUPPORT=ON/OFF` to toggle SDL support (i.e., `display_screen` and `sound` support; `OFF` by default), `-DBUILD_CPP_LIB=ON/OFF` to build\nthe `ale-lib` C++ target (`ON` by default), and `-DBUILD_PYTHON_LIB=ON/OFF` to build the pybind11 wrapper (`ON` by default).\n\nFinally, you can link agaisnt the ALE in your own CMake project as follows\n\n```cmake\nfind_package(ale REQUIRED)\ntarget_link_libraries(YourTarget ale::ale-lib)\n```\n\nCiting\n======\n\nIf you use the ALE in your research, we ask that you please cite the following.\n\n*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.*\n\nIn BibTeX format:\n\n```bibtex\n@Article{bellemare13arcade,\n author = {{Bellemare}, M.~G. and {Naddaf}, Y. and {Veness}, J. and {Bowling}, M.},\n title = {The Arcade Learning Environment: An Evaluation Platform for General Agents},\n journal = {Journal of Artificial Intelligence Research},\n year = \"2013\",\n month = \"jun\",\n volume = \"47\",\n pages = \"253--279\",\n}\n```\n\nIf you use the ALE with sticky actions (flag ``repeat_action_probability``), or if\nyou use the different game flavours (mode and difficulty switches), we ask you\nthat you also cite the following:\n\n*M. C. Machado, M. G. Bellemare, E. Talvitie, J. Veness, M. J. Hausknecht, M. Bowling. 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