Status: Maintenance (expect bug fixes and minor updates)
OpenAI Gym
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. This is the gym
open-source library, which gives you access to a standardized set of environments.
.. image:: https://travis-ci.org/openai/gym.svg?branch=master
:target: https://travis-ci.org/openai/gym
See What's New section below <#what-s-new>
_
gym
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://gym.openai.com/docs>
_ on our site. See also the FAQ <https://github.com/openai/gym/wiki/FAQ>
_.
A whitepaper for OpenAI Gym is available at http://arxiv.org/abs/1606.01540, 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
Basics
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 actions
to the
environment, and the environment replies with observations
and
rewards
(that is, a score).
The core gym
interface is Env <https://github.com/openai/gym/blob/master/gym/core.py>
_, which is
the unified environment interface. There is no interface for agents;
that part is left to you. The following are the Env
methods you
should know:
reset(self)
: Reset the environment's state. Returns observation
.
step(self, action)
: Step the environment by one timestep. Returns observation
, reward
, done
, info
.
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 2.7 or 3.5 -- 3.7.
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.
Installation
You can perform a minimal install of gym
with:
.. code:: shell
git clone https://github.com/openai/gym.git
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
pyglet
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 gitpod.io <https://gitpod.io/#https://github.com/openai/gym/blob/master/examples/agents/cem.py>
_ 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
MuJoCo has a proprietary dependency we can't set up for you. Follow
the
instructions <https://github.com/openai/mujoco-py#obtaining-the-binaries-and-license-key>
_
in the mujoco-py
package for help. As an alternative to mujoco-py
, consider PyBullet <https://github.com/openai/gym/blob/master/docs/environments.md#pybullet-robotics-environments>
_ 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]'
(or 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 1.5.0
. You can
upgrade using the following: pip install --ignore-installed
pip
. Alternatively, you can open setup.py
<https://github.com/openai/gym/blob/master/setup.py>
_ 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
xvfb-run
(on Ubuntu, install the xvfb
package):
.. 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 setup.py
<https://github.com/openai/gym/blob/master/setup.py>
_. We
maintain the lists of dependencies on a per-environment group basis.
Environments
See List of Environments <docs/environments.md>
_ and the gym site <http://gym.openai.com/envs/>
_.
For information on creating your own environments, see Creating your own Environments <docs/creating-environments.md>
_.
Examples
See the examples
directory.
- Run
examples/agents/random_agent.py <https://github.com/openai/gym/blob/master/examples/agents/random_agent.py>
_ to run a simple random agent.
- Run
examples/agents/cem.py <https://github.com/openai/gym/blob/master/examples/agents/cem.py>
_ to run an actual learning agent (using the cross-entropy method).
- Run
examples/scripts/list_envs <https://github.com/openai/gym/blob/master/examples/scripts/list_envs>
_ to generate a list of all environments.
Testing
We are using pytest <http://doc.pytest.org>
_ for tests. You can run them via:
.. code:: shell
pytest
.. _See What's New section below:
What's new
-
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)
- 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, gym.vector.make('Pong-v4',16)
.
-
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
-v3
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
xml_file
, ctrl_cost_weight
, reset_noise_scale
etc
-
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
Env
and Wrapper
subclasses should define step
, reset
, render
, close
, seed
rather than underscored method names.
- Removed the
board_game
, debugging
, safety
, parameter_tuning
environments since they're not being maintained by us at OpenAI. We encourage authors and users to create new repositories for these environments.
- Changed
MultiDiscrete
action space to range from [0, ..., n-1]
rather than [a, ..., b-1]
.
- No more
render(close=True)
, use env-specific methods to close the rendering.
- Removed
scoreboard
directory, since site doesn't exist anymore.
- Moved
gym/monitoring
to gym/wrappers/monitoring
- Add
dtype
to Space
.
- Not using python's built-in module anymore, using
gym.logger
-
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 https://github.com/openai/gym/pull/834) 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 *NoFrameSkip-v4
environments should be considered the canonical Atari
environments from now on.
-
2017-03-05: BACKWARDS INCOMPATIBILITY: The configure
method has been removed
from Env
. configure
was not used by gym
, but was used by some dependent
libraries including universe
. 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
env.monitor.start(directory)
, 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 close
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 write_upon_reset=True
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.