Skip to main content

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

Project description

The Arcade Learning Environment Arcade Learning Environment

Python PyPI Version

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 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 100 Atari 2600 games.
  • Multi-platform code (compiled and tested under macOS, Windows, and several Linux distributions).
  • Python bindings through pybind11.
  • Native support for Gymnasium, a maintained fork of OpenAI Gym.
  • C++ based vectorizer for acting in multiple ROMs at the same time.
  • Atari roms are packaged within the pip package.

Quick Start

The ALE currently supports three different interfaces: C++, Python, and Gymnasium.

Python

You simply need to install the ale-py package distributed via PyPI:

pip install ale-py

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

You can now import the ALE in your Python projects with providing a direct interface to Stella for interacting with games

from ale_py import ALEInterface, roms

ale = ALEInterface()
ale.loadROM(roms.get_rom_path("breakout"))
ale.reset_game()

reward = ale.act(0)  # noop
screen_obs = ale.getScreenRGB()

Gymnasium

For simplicity for installing ale-py with Gymnasium, pip install "gymnasium[atari]" shall install all necessary modules and ROMs. See Gymnasium introductory page for description of the API to interface with the environment.

import gymnasium as gym
import ale_py

gym.register_envs(ale_py)  # unnecessary but helpful for IDEs

env = gym.make('ALE/Breakout-v5', render_mode="human")  # remove render_mode in training
obs, info = env.reset()
episode_over = False
while not episode_over:
    action = policy(obs)  # to implement - use `env.action_space.sample()` for a random policy
    obs, reward, terminated, truncated, info = env.step(action)

    episode_over = terminated or truncated
env.close()

To run with continuous actions, you can simply modify the call to gym.make above with:

env = gym.make('ALE/Breakout-v5', continuous=True, render_mode="human")

For all the environments available and their description, see gymnasium atari page.

A vectorized environment with preprocessing, written in C++, is also available with gym.make_vec("ALE/Breakout-v5", num_envs=10). See vector-environment for more information.

C++

The following instructions will assume you have a valid C++17 compiler and 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

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 against 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}
}

If you use the CALE (Continuous ALE), we ask you that you also cite the following:

Jesse Farebrother and Pablo Samuel Castro. Cale: Continuous arcade learning environment.Ad-vances in Neural Information Processing Systems, 2024.

In BibTex format:

@article{farebrother2024cale,
  title={C{ALE}: Continuous Arcade Learning Environment},
  author={Jesse Farebrother and Pablo Samuel Castro},
  journal={Advances in Neural Information Processing Systems},
  year={2024}
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ale_py-0.11.2.tar.gz (512.3 kB view details)

Uploaded Source

Built Distributions

ale_py-0.11.2-cp313-cp313-win_amd64.whl (3.5 MB view details)

Uploaded CPython 3.13Windows x86-64

ale_py-0.11.2-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (5.1 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ x86-64

ale_py-0.11.2-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl (5.1 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ ARM64

ale_py-0.11.2-cp313-cp313-macosx_13_0_x86_64.whl (2.5 MB view details)

Uploaded CPython 3.13macOS 13.0+ x86-64

ale_py-0.11.2-cp313-cp313-macosx_13_0_arm64.whl (2.3 MB view details)

Uploaded CPython 3.13macOS 13.0+ ARM64

ale_py-0.11.2-cp312-cp312-win_amd64.whl (3.5 MB view details)

Uploaded CPython 3.12Windows x86-64

ale_py-0.11.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (5.1 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ x86-64

ale_py-0.11.2-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl (5.1 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ ARM64

ale_py-0.11.2-cp312-cp312-macosx_13_0_x86_64.whl (2.5 MB view details)

Uploaded CPython 3.12macOS 13.0+ x86-64

ale_py-0.11.2-cp312-cp312-macosx_13_0_arm64.whl (2.3 MB view details)

Uploaded CPython 3.12macOS 13.0+ ARM64

ale_py-0.11.2-cp311-cp311-win_amd64.whl (3.5 MB view details)

Uploaded CPython 3.11Windows x86-64

ale_py-0.11.2-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (5.1 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

ale_py-0.11.2-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl (5.1 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ ARM64

ale_py-0.11.2-cp311-cp311-macosx_13_0_x86_64.whl (2.5 MB view details)

Uploaded CPython 3.11macOS 13.0+ x86-64

ale_py-0.11.2-cp311-cp311-macosx_13_0_arm64.whl (2.3 MB view details)

Uploaded CPython 3.11macOS 13.0+ ARM64

ale_py-0.11.2-cp310-cp310-win_amd64.whl (3.5 MB view details)

Uploaded CPython 3.10Windows x86-64

ale_py-0.11.2-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (5.1 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

ale_py-0.11.2-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl (5.1 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ ARM64

ale_py-0.11.2-cp310-cp310-macosx_13_0_x86_64.whl (2.5 MB view details)

Uploaded CPython 3.10macOS 13.0+ x86-64

ale_py-0.11.2-cp310-cp310-macosx_13_0_arm64.whl (2.3 MB view details)

Uploaded CPython 3.10macOS 13.0+ ARM64

ale_py-0.11.2-cp39-cp39-win_amd64.whl (3.5 MB view details)

Uploaded CPython 3.9Windows x86-64

ale_py-0.11.2-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (5.0 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

ale_py-0.11.2-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl (5.0 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ ARM64

ale_py-0.11.2-cp39-cp39-macosx_13_0_x86_64.whl (2.5 MB view details)

Uploaded CPython 3.9macOS 13.0+ x86-64

ale_py-0.11.2-cp39-cp39-macosx_13_0_arm64.whl (2.3 MB view details)

Uploaded CPython 3.9macOS 13.0+ ARM64

File details

Details for the file ale_py-0.11.2.tar.gz.

File metadata

  • Download URL: ale_py-0.11.2.tar.gz
  • Upload date:
  • Size: 512.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for ale_py-0.11.2.tar.gz
Algorithm Hash digest
SHA256 cc6fed9d994d796d76b0d042fbe0a7101834faa55b5e70b1e1f14367ed9e2a8a
MD5 af939abca17f5006a6985e0d280a0697
BLAKE2b-256 2c85fffbe95501efc9ecf73e02fb62e7a99401e12dcb8217313016dd00b13cdc

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: ale_py-0.11.2-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 3.5 MB
  • Tags: CPython 3.13, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for ale_py-0.11.2-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 868019090c66fc8c2c24fb19dd8e956a5a4211e594b78097ce1db11a5736684e
MD5 b2515200bbc458f8606cd87a8c340ce3
BLAKE2b-256 bf6e365b95c82b214e6b193c6dbda94afaedc5b1673c77777c3b3e3cabd75761

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for ale_py-0.11.2-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 09e56970ae5f56f377d1c6d8d364d0c610f9c0bc4f88f7abce48174c83ea2882
MD5 94f6089b7ba46fc69af8bac697ea970c
BLAKE2b-256 3b1b79bf78c840d520b6928480bee14b39c3617833edb9ccf81a965e9beec30a

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl.

File metadata

File hashes

Hashes for ale_py-0.11.2-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl
Algorithm Hash digest
SHA256 d12e62ac6d57a02745ad8cbf72fbf11ffedbe12d14b48d08e33f22f5625c8ec8
MD5 781468dc29a80cd2a0737fb6bd6c6700
BLAKE2b-256 e577e4b21f4befd96c949aa9addf543cf612504612df30039841a182831fb593

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp313-cp313-macosx_13_0_x86_64.whl.

File metadata

File hashes

Hashes for ale_py-0.11.2-cp313-cp313-macosx_13_0_x86_64.whl
Algorithm Hash digest
SHA256 f12a9ee789c3c851ea60afe91c6273e49b880dca510bae00496b0339c41cda81
MD5 06a5359fb71c443bb85997b604ca3b6e
BLAKE2b-256 aa02ec9804b7f6ad80ecb9c4b7f76bad6f672fca3337ea3d7662bdb7239aa595

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp313-cp313-macosx_13_0_arm64.whl.

File metadata

File hashes

Hashes for ale_py-0.11.2-cp313-cp313-macosx_13_0_arm64.whl
Algorithm Hash digest
SHA256 208c70a8a47d8ba5f0b6eb8bfd1c3adc6ec2718f66d7866646976f0853dbda4e
MD5 53a92aa11e936c0655636f3d040210f5
BLAKE2b-256 8527a75f6c231704af0a71d15fa3520d4f3444031fecb614ba96b2b567c4d873

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: ale_py-0.11.2-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 3.5 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for ale_py-0.11.2-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 bb8c4d6d8b6cbecfff2915c9f1787101f033719b66f8149dbc4685a2ff22514a
MD5 6ea9fe7f992f20c71d8c26d249057eb0
BLAKE2b-256 4bdc55f404bf4c8a2c707ef05eba9a8986fc943d192e26d2b4bb6889de02abc9

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for ale_py-0.11.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 212927f98357390e651b830835e1d24690816416d0e0d2148ad2d4c679941e1c
MD5 6b986e8389cce65fe28131ebb75f2965
BLAKE2b-256 1aff154caf6b05b7993dd438b78bcd2a8c7d97c6ecd4a3fee82a610d83711b84

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl.

File metadata

File hashes

Hashes for ale_py-0.11.2-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl
Algorithm Hash digest
SHA256 7c42fa8a76caf04dd435bd3fc8682a9d25128102d1df96c35b7971ee31f137d0
MD5 00ac35e15e24859c237ec67c137d98fa
BLAKE2b-256 42de942044cadbbba73a2c15f1e9a6603106afd53a8c1e6c91ec6eca0e8ca021

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp312-cp312-macosx_13_0_x86_64.whl.

File metadata

File hashes

Hashes for ale_py-0.11.2-cp312-cp312-macosx_13_0_x86_64.whl
Algorithm Hash digest
SHA256 db47d52e75ee0bc08899e32e3c2b05822c3a75f4e6f34e7896bd1133bec3dee7
MD5 91e290cd8af0bb78d32c5e1c7e304555
BLAKE2b-256 e15821de6504fd571ea827d71d436ca196d1253de7a6d2ba26b4e0c19c08d2b4

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp312-cp312-macosx_13_0_arm64.whl.

File metadata

File hashes

Hashes for ale_py-0.11.2-cp312-cp312-macosx_13_0_arm64.whl
Algorithm Hash digest
SHA256 5ab4bfac7c17fdbd96e8068424f491a16c18584f7bbe2797cbb6c13cc4930e76
MD5 716692f96ebd38b885f5f6dc77950e33
BLAKE2b-256 fab6e71cf04cc71eb0131aeb31be51440ca2081bcdfe44af06a09dee5fab0431

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: ale_py-0.11.2-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 3.5 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for ale_py-0.11.2-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 858a644ed92409cdef47a88d177d18421260b74d3f5cdb45963f21de870a6fd9
MD5 1a286161246a0848873aeb2fc3d5574f
BLAKE2b-256 8f41a7de8030021b478e8829b47e8667e79c910c3bb1539d72b6c8052b14cc3c

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for ale_py-0.11.2-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 f7a2082e00fc81b6706daf945bd4c97b69c5542739707638c65ddf65ad74db38
MD5 b840659fec49f82db8459d28bbdba335
BLAKE2b-256 3d92623e8b3157fdd39a0d78026a1c2727c9215d882d67bfb72697661c7dba6f

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl.

File metadata

File hashes

Hashes for ale_py-0.11.2-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl
Algorithm Hash digest
SHA256 44838121ab5c2ef50033ebf0cc69aadf3954418d2f8812bdb76fced3797eb33f
MD5 637a234dda447261b1c573808cb955c4
BLAKE2b-256 1339293fab6925966076112a49683ac71ccfe6cd75a1790856e90e3ef0a11bd1

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp311-cp311-macosx_13_0_x86_64.whl.

File metadata

File hashes

Hashes for ale_py-0.11.2-cp311-cp311-macosx_13_0_x86_64.whl
Algorithm Hash digest
SHA256 d80311cf92ca6ca777dec363865891dbb5447e0c9f57774f72c8618851c9fd4b
MD5 95513bb60823400c1cb3d5366f784f43
BLAKE2b-256 7b009ec89f60c14a378096f23c190a60155b9551e521a00046dd8bb3279b00ea

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp311-cp311-macosx_13_0_arm64.whl.

File metadata

File hashes

Hashes for ale_py-0.11.2-cp311-cp311-macosx_13_0_arm64.whl
Algorithm Hash digest
SHA256 808c98685a607cc5483238f73915c23426537259f9cece506f47f5213c370734
MD5 334a2e7fcb04ccb8fe457d8501426004
BLAKE2b-256 c39cac9735b7a8776323fba54a0a3a8eac4706edb8fdac8a621516550b77ee14

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: ale_py-0.11.2-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 3.5 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for ale_py-0.11.2-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 b70ab0eee7f5215dc2ab047b7c3e1d76a524d6764d496c2a6512c3a0beb96f70
MD5 fdd493ce029b5ad46aec357c73ffc586
BLAKE2b-256 26f6f1ed23ce9664291ea476fdb984363cd4075c6645114087472fdce2893f9c

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for ale_py-0.11.2-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 a8a2777db64e181faf69318aaf8098769ee48b84e377d6f8163c024a54967bf8
MD5 bf9398f17b78b7d32df7c5cac0fc9fff
BLAKE2b-256 a3627cd40874f0b0a7a59d890135d4887669ebaf9c1e4c850e8f7dd1f1ba2b31

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl.

File metadata

File hashes

Hashes for ale_py-0.11.2-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl
Algorithm Hash digest
SHA256 cdb8ce821c70bc60dfca1871b0b1608ba5d269e56370aad7aaae62a698d3746d
MD5 a5fd16eb2f5668f63e0d91235d16568f
BLAKE2b-256 aebfb5b5f33481deee327c5c57dd1a7f0cd92e34e0a5ce749d3ff93f7aad7459

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp310-cp310-macosx_13_0_x86_64.whl.

File metadata

File hashes

Hashes for ale_py-0.11.2-cp310-cp310-macosx_13_0_x86_64.whl
Algorithm Hash digest
SHA256 c9730aa819fac17915fa72fe85feaeaa4c5181616af0783b3cb340930bfd285f
MD5 d3f332cde9ba70ca3d6fdfcc6a6cb778
BLAKE2b-256 d7f0eceff946e855c6900590cdfa899ac7cad74912f91cbf628b12043846425e

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp310-cp310-macosx_13_0_arm64.whl.

File metadata

File hashes

Hashes for ale_py-0.11.2-cp310-cp310-macosx_13_0_arm64.whl
Algorithm Hash digest
SHA256 58f18a60cdb6d48f7a4eb978327965c121674333a622a92ba60250776d8351c6
MD5 51a194ccde9a3ca6174b342f64b02f1c
BLAKE2b-256 9c0bbba78b2cc30d4c96c6d23c80c8d1334114b6f02ac66082dc50ffba6764ab

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: ale_py-0.11.2-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 3.5 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for ale_py-0.11.2-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 c82eae022713a62b0fc580134108eb8895ebe5e4ff61ee3e9626c267ecf3fac7
MD5 6c14a187fa19b375e3a676fdec5ca7a1
BLAKE2b-256 2b0e41f72b2c052df0bc11ea271e2aa60b1ec1e9837b8b7f61d538f8f9589e94

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.

File metadata

File hashes

Hashes for ale_py-0.11.2-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
Algorithm Hash digest
SHA256 8c09ce4980ccc6d7c94b4a6d8fd296bc5b6ff2538946a8cc648b7b9d95f9553b
MD5 ccabaa7829499120345dd84b52b71559
BLAKE2b-256 34af6993586f3f87ed416d70eea1dea1588b76c5277c7fdfdcc223109cc1095a

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl.

File metadata

File hashes

Hashes for ale_py-0.11.2-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl
Algorithm Hash digest
SHA256 b89fb1a53ab57e1d8c9539f5004aa8afb405620d1fbab6e05fd6b341b7551110
MD5 348b17a7f3fcf5220befed9c1a9f5be7
BLAKE2b-256 f7ad120f97512e6e43957331c85dc1d672a845fcac4d3371b9e79d6b1492c720

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp39-cp39-macosx_13_0_x86_64.whl.

File metadata

File hashes

Hashes for ale_py-0.11.2-cp39-cp39-macosx_13_0_x86_64.whl
Algorithm Hash digest
SHA256 6e4cc490a09495278a08355449ff445d46461fc2cb998fbb8fba7f9c0dc59deb
MD5 665e38d92f6acd1e2a422e7d394db420
BLAKE2b-256 8095715806343a85c112a2f26bdc815bf896dad005a914ccf6f121d38d85b333

See more details on using hashes here.

File details

Details for the file ale_py-0.11.2-cp39-cp39-macosx_13_0_arm64.whl.

File metadata

File hashes

Hashes for ale_py-0.11.2-cp39-cp39-macosx_13_0_arm64.whl
Algorithm Hash digest
SHA256 eb70b789ad03a2fe221185a07365f0b740f81ec378de87189a759efeeb4a8f6b
MD5 961ea9971c5b4f94c6f823b2d2057da6
BLAKE2b-256 a609e04eb8734897d3aa2242813f6aeecf32c04a06ce8cffd61a492edd2082ca

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page