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A toolkit for developing and comparing reinforcement learning algorithms.

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OpenAI Gym

OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. This is the


open-source library, which gives you access to a standardized set of environments.

.. image:: :target:

See What's New section below



makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. You can use it from Python code, and soon from other languages.

If you're not sure where to start, we recommend beginning with the

docs <https:></https:>

_ on our site. See also the

FAQ <https:></https:>


A whitepaper for OpenAI Gym is available at, and here's a BibTeX entry that you can use to cite it in a publication::

@misc{1606.01540, Author = {Greg Brockman and Vicki Cheung and Ludwig Pettersson and Jonas Schneider and John Schulman and Jie Tang and Wojciech Zaremba}, Title = {OpenAI Gym}, Year = {2016}, Eprint = {arXiv:1606.01540}, }

.. contents:: Contents of this document :depth: 2


There are two basic concepts in reinforcement learning: the environment (namely, the outside world) and the agent (namely, the algorithm you are writing). The agent sends


to the environment, and the environment replies with




(that is, a score).

The core


interface is

Env <https:></https:>

_, which is the unified environment interface. There is no interface for agents; that part is left to you. The following are the


methods you should know:


: Reset the environment's state. Returns



step(self, action)

: Step the environment by one timestep. Returns









render(self, mode='human')

: Render one frame of the environment. The default mode will do something human friendly, such as pop up a window.

Supported systems

We currently support Linux and OS X running Python 3.5 -- 3.8 Windows support is experimental - algorithmic, toy_text, classic_control and atari should work on Windows (see next section for installation instructions); nevertheless, proceed at your own risk.


You can perform a minimal install of



.. code:: shell

git clone cd gym pip install -e .

If you prefer, you can do a minimal install of the packaged version directly from PyPI:

.. code:: shell

pip install gym

You'll be able to run a few environments right away:

  • algorithmic
  • toy_text
  • classic_control (you'll need
    to render though)

We recommend playing with those environments at first, and then later installing the dependencies for the remaining environments.

You can also

run gym on <https:></https:>

_ to play with the examples online.
In the preview window you can click on the mp4 file you want to view. If you want to view another mp4 file, just press the back button and click on another mp4 file.

Installing everything

To install the full set of environments, you'll need to have some system packages installed. We'll build out the list here over time; please let us know what you end up installing on your platform. Also, take a look at the docker files (py.Dockerfile) to see the composition of our CI-tested images.

On Ubuntu 16.04 and 18.04:

.. code:: shell

apt-get install -y libglu1-mesa-dev libgl1-mesa-dev libosmesa6-dev xvfb ffmpeg curl patchelf libglfw3 libglfw3-dev cmake zlib1g zlib1g-dev swig

MuJoCo has a proprietary dependency we can't set up for you. Follow the

instructions <https:></https:>

_ in the


package for help. As an alternative to


, consider

PyBullet <https:></https:>

_ which uses the open source Bullet physics engine and has no license requirement.

Once you're ready to install everything, run

pip install -e '.[all]'


pip install 'gym[all]'


Pip version

To run

pip install -e '.[all]'

, you'll need a semi-recent pip. Please make sure your pip is at least at version


. You can upgrade using the following:

pip install --ignore-installed pip

. Alternatively, you can open<https:></https:>

_ and install the dependencies by hand.

Rendering on a server

If you're trying to render video on a server, you'll need to connect a fake display. The easiest way to do this is by running under


(on Ubuntu, install the



.. code:: shell

xvfb-run -s "-screen 0 1400x900x24" bash

Installing dependencies for specific environments

If you'd like to install the dependencies for only specific environments, see<https:></https:>

_. We maintain the lists of dependencies on a per-environment group basis.



List of Environments <docs></docs>

_ and the

gym site <http:></http:>


For information on creating your own environments, see

Creating your own Environments <docs></docs>



See the



  • Run
    examples/agents/random\ <https:></https:>
    _ to run a simple random agent.
  • Run
    examples/agents/ <https:></https:>
    _ to run an actual learning agent (using the cross-entropy method).
  • Run
    examples/scripts/list\_envs <https:></https:>
    _ to generate a list of all environments.


We are using

pytest <http:></http:>

_ for tests. You can run them via:

.. code:: shell


.. _See What's New section below:

What's new

  • 2020-05-08 (v 0.17.2)

    • remove unnecessary precision warning when creating Box with scalar bounds - thanks @johannespitz!
    • remove six from the dependencies
    • FetchEnv sample goal range can be specified through kwargs - thanks @YangRui2015!

2020-03-05 (v 0.17.1)

  • update cloudpickle dependency to be >=1.2.0,<1.4.0

2020-02-21 (v 0.17.0)

  • Drop python 2 support
  • Add python 3.8 build

2020-02-09 (v 0.16.0)

  • EnvSpec API change - remove tags field (retro-active version bump, the changes are actually already in the codebase since 0.15.5 - thanks @wookayin for keeping us in check!)

2020-02-03 (v0.15.6)

  • pyglet 1.4 compatibility (this time for real :))
  • Fixed the bug in BipedalWalker and BipedalWalkerHardcore, bumped version to 3 (thanks @chozabu!)

2020-01-24 (v0.15.5)

  • pyglet 1.4 compatibility
  • remove python-opencv from the requirements

2019-11-08 (v0.15.4)

  • Added multiple env wrappers (thanks @zuoxingdong and @hartikainen!)
  • Removed mujoco >= 2.0 support due to lack of tests

2019-10-09 (v0.15.3)

  • VectorEnv modifications - unified the VectorEnv api (added reset_async, reset_wait, step_async, step_wait methods to SyncVectorEnv); more flexibility in AsyncVectorEnv workers

2019-08-23 (v0.15.2)

  • More Wrappers - AtariPreprocessing, FrameStack, GrayScaleObservation, FilterObservation, FlattenDictObservationsWrapper, PixelObservationWrapper, TransformReward (thanks @zuoxingdong, @hartikainen)
  • Remove rgb_rendering_tracking logic from mujoco environments (default behavior stays the same for the -v3 environments, rgb rendering returns a view from tracking camera)
  • Velocity goal constraint for MountainCar (thanks @abhinavsagar)
  • Taxi-v2 -> Taxi-v3 (add missing wall in the map to replicate env as describe in the original paper, thanks @kobotics)

2019-07-26 (v0.14.0)

  • Wrapper cleanup
  • Spec-related bug fixes
  • VectorEnv fixes

2019-06-21 (v0.13.1)

  • Bug fix for ALE 0.6 difficulty modes
  • Use narrow range for pyglet versions

2019-06-21 (v0.13.0)

  • Upgrade to ALE 0.6 (atari-py 0.2.0) (thanks @JesseFarebro!)

2019-06-21 (v0.12.6)

  • Added vectorized environments (thanks @tristandeleu!). Vectorized environment runs multiple copies of an environment in parallel. To create a vectorized version of an environment, use
    gym.vector.make(env\_id, num\_envs, \*\*kwargs)
    , for instance,


2019-05-28 (v0.12.5)

  • fixed Fetch-slide environment to be solvable.

2019-05-24 (v0.12.4)

  • remove pyopengl dependency and use more narrow atari-py and box2d-py versions

2019-03-25 (v0.12.1)

  • rgb rendering in MuJoCo locomotion

    environments now comes from tracking camera (so that agent does not run away from the field of view). The old behaviour can be restored by passing rgb_rendering_tracking=False kwarg. Also, a potentially breaking change!!! Wrapper class now forwards methods and attributes to wrapped env.

2019-02-26 (v0.12.0)

  • release mujoco environments v3 with support for gym.make kwargs such as


2019-02-06 (v0.11.0)

  • remove gym.spaces.np_random common PRNG; use per-instance PRNG instead.
  • support for kwargs in gym.make
  • lots of bugfixes
    • 2018-02-28: Release of a set of new robotics environments.
  • 2018-01-25: Made some aesthetic improvements and removed unmaintained parts of gym. This may seem like a downgrade in functionality, but it is actually a long-needed cleanup in preparation for some great new things that will be released in the next month.

    • Now your
      subclasses should define
      rather than underscored method names.
    • Removed the
      environments since they're not being maintained by us at OpenAI. We encourage authors and users to create new repositories for these environments.
    • Changed
      action space to range from
      [0, ..., n-1]
      rather than
      [a, ..., b-1]
    • No more
      , use env-specific methods to close the rendering.
    • Removed
      directory, since site doesn't exist anymore.
    • Moved
    • Add
    • Not using python's built-in module anymore, using
  • 2018-01-24: All continuous control environments now use mujoco_py >= 1.50. Versions have been updated accordingly to -v2, e.g. HalfCheetah-v2. Performance should be similar (see but there are likely some differences due to changes in MuJoCo.

  • 2017-06-16: Make env.spec into a property to fix a bug that occurs when you try to print out an unregistered Env.

2017-05-13: BACKWARDS INCOMPATIBILITY: The Atari environments are now at_v4_. To keep using the old v3 environments, keep gym <= 0.8.2 and atari-py <= 0.0.21. Note that the v4 environments will not give identical results to existing v3 results, although differences are minor. The v4 environments incorporate the latest Arcade Learning Environment (ALE), including several ROM fixes, and now handle loading and saving of the emulator state. While seeds still ensure determinism, the effect of any given seed is not preserved across this upgrade because the random number generator in ALE has changed. The


environments should be considered the canonical Atari environments from now on.



method has been removed from




was not used by


, but was used by some dependent libraries including


. These libraries will migrate away from the configure method by using wrappers instead. This change is on master and will be released with 0.8.0.

2016-12-27: BACKWARDS INCOMPATIBILITY: The gym monitor is now a wrapper. Rather than starting monitoring as


, envs are now wrapped as follows:

env = wrappers.Monitor(env, directory)

. This change is on master and will be released with 0.7.0.

  • 2016-11-1: Several experimental changes to how a running monitor interacts with environments. The monitor will now raise an error if reset() is called when the env has not returned done=True. The monitor will only record complete episodes where done=True. Finally, the monitor no longer calls seed() on the underlying env, nor does it record or upload seed information.

  • 2016-10-31: We're experimentally expanding the environment ID format to include an optional username.

  • 2016-09-21: Switch the Gym automated logger setup to configure the root logger rather than just the 'gym' logger.

2016-08-17: Calling


on an env will also close the monitor and any rendering windows.

2016-08-17: The monitor will no longer write manifest files in real-time, unless


is passed.

  • 2016-05-28: For controlled reproducibility, envs now support seeding (cf #91 and #135). The monitor records which seeds are used. We will soon add seed information to the display on the scoreboard.

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