Skip to main content

No project description provided

Project description

Status: Maintenance (expect bug fixes and minor updates)

Gym Retro

Gym Retro lets you turn classic video games into Gym environments for reinforcement learning and comes with integrations for ~1000 games. It uses various emulators that support the Libretro API, making it fairly easy to add new emulators.

Supported platforms:

  • Windows 7, 8, 10
  • macOS 10.13 (High Sierra), 10.14 (Mojave)
  • Linux (manylinux1)

Supported Pythons:

  • 3.6
  • 3.7
  • 3.8

Each game integration has files listing memory locations for in-game variables, reward functions based on those variables, episode end conditions, savestates at the beginning of levels and a file containing hashes of ROMs that work with these files.

Please note that ROMs are not included and you must obtain them yourself. Most ROM hashes are sourced from their respective No-Intro SHA-1 sums.

Documentation

Documentation is available at https://retro.readthedocs.io/en/latest/

You should probably start with the Getting Started Guide.

Contributing

See CONTRIBUTING.md

Changelog

See CHANGES.md

Emulated Systems

  • Atari
    • Atari2600 (via Stella)
  • NEC
    • TurboGrafx-16/PC Engine (via Mednafen/Beetle PCE Fast)
  • Nintendo
    • Game Boy/Game Boy Color (via gambatte)
    • Game Boy Advance (via mGBA)
    • Nintendo Entertainment System (via FCEUmm)
    • Super Nintendo Entertainment System (via Snes9x)
  • Sega
    • GameGear (via Genesis Plus GX)
    • Genesis/Mega Drive (via Genesis Plus GX)
    • Master System (via Genesis Plus GX)

See LICENSES.md for information on the licenses of the individual cores.

Included ROMs

The following non-commercial ROMs are included with Gym Retro for testing purposes:

Citation

Please cite using the following BibTeX entry:

@article{nichol2018retro,
  title={Gotta Learn Fast: A New Benchmark for Generalization in RL},
  author={Nichol, Alex and Pfau, Vicki and Hesse, Christopher and Klimov, Oleg and Schulman, John},
  journal={arXiv preprint arXiv:1804.03720},
  year={2018}
}

Project details


Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

gym_retro-0.8.0-cp38-cp38-win_amd64.whl (152.0 MB view details)

Uploaded CPython 3.8 Windows x86-64

gym_retro-0.8.0-cp38-cp38-manylinux1_x86_64.whl (161.9 MB view details)

Uploaded CPython 3.8

gym_retro-0.8.0-cp38-cp38-macosx_10_13_x86_64.whl (146.1 MB view details)

Uploaded CPython 3.8 macOS 10.13+ x86-64

gym_retro-0.8.0-cp37-cp37m-win_amd64.whl (152.0 MB view details)

Uploaded CPython 3.7m Windows x86-64

gym_retro-0.8.0-cp37-cp37m-manylinux1_x86_64.whl (162.0 MB view details)

Uploaded CPython 3.7m

gym_retro-0.8.0-cp37-cp37m-macosx_10_13_x86_64.whl (146.1 MB view details)

Uploaded CPython 3.7m macOS 10.13+ x86-64

gym_retro-0.8.0-cp36-cp36m-win_amd64.whl (152.0 MB view details)

Uploaded CPython 3.6m Windows x86-64

gym_retro-0.8.0-cp36-cp36m-manylinux1_x86_64.whl (162.0 MB view details)

Uploaded CPython 3.6m

gym_retro-0.8.0-cp36-cp36m-macosx_10_13_x86_64.whl (146.1 MB view details)

Uploaded CPython 3.6m macOS 10.13+ x86-64

File details

Details for the file gym_retro-0.8.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: gym_retro-0.8.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 152.0 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.3

File hashes

Hashes for gym_retro-0.8.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 410ba891b52bdee1d1f658116bd2715ed84cc23bf62e8b7b1b862e39b04dbc9c
MD5 cf9d2bd7132c078b68cb76dc18f98f01
BLAKE2b-256 cacec102fc8e527cf7be8c8bfa419a9030e1a124e0372ab2fb058773dd5468e3

See more details on using hashes here.

File details

Details for the file gym_retro-0.8.0-cp38-cp38-manylinux1_x86_64.whl.

File metadata

  • Download URL: gym_retro-0.8.0-cp38-cp38-manylinux1_x86_64.whl
  • Upload date:
  • Size: 161.9 MB
  • Tags: CPython 3.8
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.3

File hashes

Hashes for gym_retro-0.8.0-cp38-cp38-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 b10e47b5c5f3ba9adae46e70e71bf3df0f53d07feaad904b5e5bb20fbafb9057
MD5 2a222a9901c6788cb67d8dda868e0b50
BLAKE2b-256 c0d939a89bbba20f244f328dbb893005160818f4dd9e932fa7e335d3df6aa776

See more details on using hashes here.

File details

Details for the file gym_retro-0.8.0-cp38-cp38-macosx_10_13_x86_64.whl.

File metadata

  • Download URL: gym_retro-0.8.0-cp38-cp38-macosx_10_13_x86_64.whl
  • Upload date:
  • Size: 146.1 MB
  • Tags: CPython 3.8, macOS 10.13+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.3

File hashes

Hashes for gym_retro-0.8.0-cp38-cp38-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 7c8684c456c15c8834d00c31e4628e4424e4cd5920fb0f09a60d4787f2dcf485
MD5 71cbd643e91bd23955e9d23d457160ce
BLAKE2b-256 53d56204ed9546eeb6344d500450f566329fbaaf0d4a7e250be1f01ae2290807

See more details on using hashes here.

File details

Details for the file gym_retro-0.8.0-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: gym_retro-0.8.0-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 152.0 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.3

File hashes

Hashes for gym_retro-0.8.0-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 5c0b993e8f90ac8cc1f0bc4beb227043bef0fc7530c58486a3f7662fc9853a8b
MD5 d4f6169e68476b34be0c9557c4b4d809
BLAKE2b-256 dc6c6c452432c5b12bfd6e30067bfbdcceaee998b5ff2ab4f35a96088230ed4b

See more details on using hashes here.

File details

Details for the file gym_retro-0.8.0-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

  • Download URL: gym_retro-0.8.0-cp37-cp37m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 162.0 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.3

File hashes

Hashes for gym_retro-0.8.0-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 4c3205faf3874f7f103fe2a9d5e3f097614ae9869bd22972657d6eea42df80ee
MD5 df96143cf80243f53f5c0263821dd42c
BLAKE2b-256 ccf0d7cc8f49be83c8608d5f707650a917a85e656ea51684aa434a1b6b8024fe

See more details on using hashes here.

File details

Details for the file gym_retro-0.8.0-cp37-cp37m-macosx_10_13_x86_64.whl.

File metadata

  • Download URL: gym_retro-0.8.0-cp37-cp37m-macosx_10_13_x86_64.whl
  • Upload date:
  • Size: 146.1 MB
  • Tags: CPython 3.7m, macOS 10.13+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.3

File hashes

Hashes for gym_retro-0.8.0-cp37-cp37m-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 dbbdfae7f21d6a3b9b9b73da06f9ddcb031cbb59e13bce62d463edc90e9cd4a1
MD5 db9c7d486bcf1b5d966990f4937c56b5
BLAKE2b-256 880d71a99155e7efc39bb91c142b5dafdb8575b84a4b3702eac821d3b08b498a

See more details on using hashes here.

File details

Details for the file gym_retro-0.8.0-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: gym_retro-0.8.0-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 152.0 MB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.3

File hashes

Hashes for gym_retro-0.8.0-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 edb0d1d27fb6b8eb901e2affb366c9ec9c650a15f215bc4e83fe650c91b02dd1
MD5 9ba2159c40d11179f6406ad23dbd7039
BLAKE2b-256 93f271cace3e1de168a9eee71d285c0e6c6552f4ad3400759cf8ea0d9d77a902

See more details on using hashes here.

File details

Details for the file gym_retro-0.8.0-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

  • Download URL: gym_retro-0.8.0-cp36-cp36m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 162.0 MB
  • Tags: CPython 3.6m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.3

File hashes

Hashes for gym_retro-0.8.0-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 cd60c812b81e4c888454f574fa22e791e9e7a9d72045c9e17399ebd7c156b372
MD5 ce0dec1eb2ce8ac9b4178a3c2f709a96
BLAKE2b-256 cd3deb88732eec7bdc13a605c1ba655b34f911af36b894b8c1c377dabb0547f4

See more details on using hashes here.

File details

Details for the file gym_retro-0.8.0-cp36-cp36m-macosx_10_13_x86_64.whl.

File metadata

  • Download URL: gym_retro-0.8.0-cp36-cp36m-macosx_10_13_x86_64.whl
  • Upload date:
  • Size: 146.1 MB
  • Tags: CPython 3.6m, macOS 10.13+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.3

File hashes

Hashes for gym_retro-0.8.0-cp36-cp36m-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 29e1bd37e7090c3b121d793e06d5b0ef3b283e9915c0f5a9abc2b7b18c660ffb
MD5 24c73d3e0c2f9bd7e7d488021d4f67c1
BLAKE2b-256 0aeaffef74b38b78123061cd05394140feb6162214b134bdcaa607b50a3f6262

See more details on using hashes here.

Supported by

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