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 an ever-growing variety of environments.

``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.

.. 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`, `action`, `reward`, `done`.
- `render(self, mode='human', close=False)`: Render one frame of the environment. The default mode will do something human friendly, such as pop up a window. Passing the `close` flag signals the renderer to close any such windows.

Installation
============

You can perform a minimal install of ``gym`` with:

.. code:: shell
	  
	  git clone git@github.com:gym
	  cd gym
	  pip install -e .

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

- `algorithmic <https://gym.openai.com/envs#algorithmic>`_
- `toy_text <https://gym.openai.com/envs#toy_text>`_
- `classic_control <https://gym.openai.com/envs#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.

Installing everything
---------------------

Once you're ready to install everything, run ``pip install -e .[all]``.

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.

For the install to succeed, 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.

On Ubuntu 14.04:

.. code:: shell
	  
	  apt-get install -y numpy python-dev cmake zlib1g-dev libjpeg-dev xvfb libav-tools xorg-dev python-opengl

Supported systems
-----------------

We currenty support Python 2.7 on Linux and OSX.

We will expand support to Python 3 and Windows based on demand. We
will also soon ship a Docker container exposing OpenAI Gym as an API
callable from any platform.

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.

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
============

The code for each environment group is housed in its own subdirectory
`gym/envs
<https://github.com/openai/gym/blob/master/gym/envs>`_. The
specification of each task is in `gym/envs/__init__.py
<https://github.com/openai/gym/blob/master/gym/envs/__init__.py>`_. It's
worth browsing through both.

Algorithmic
-----------

These are a variety of algorithmic tasks, such as learning to copy a
sequence.

.. code:: python

	  import gym
	  env = gym.make('Copy-v0')
	  env.reset()
	  env.render()

Atari
-----

The Atari environments are a variety of Atari video games. If you didn't do the full install, you can install dependencies via ``pip install -e .[atari]`` and then get started as follow:

.. code:: python

	  import gym
	  env = gym.make('SpaceInvaders-v0')
	  env.reset()
	  env.render()

This will install ``atari-py``, which automatically compiles the `Arcade Learning Environment <http://www.arcadelearningenvironment.org/>`_. This can take quite a while (a few minutes on a decent laptop), so just be prepared.

Board games
-----------

The board game environments are a variety of board games. If you didn't do the full install, you can install dependencies via ``pip install -e .[board_game]`` and then get started as follow:

.. code:: python

	  import gym
	  env = gym.make('Go9x9-v0')
	  env.reset()
	  env.render()

Classic control
---------------

These are a variety of classic control tasks, which would appear in a typical reinforcement learning textbook. If you didn't do the full install, you will need to run ``pip install -e .[classic_control]`` to enable rendering. You can get started with them via:

.. code:: python

	  import gym
	  env = gym.make('CartPole-v0')
	  env.reset()
	  env.render()

MuJoCo
------

`MuJoCo <http://www.mujoco.org/>`_ is a physics engine which can do
very detailed efficient simulations with contacts. It's not
open-source, so you'll have to follow the instructions in `mujoco-py
<https://github.com/openai/mujoco-py#obtaining-the-binaries-and-license-key>`_
to set it up. You'll have to also run ``pip install -e .[mujoco]`` if you didn't do the full install.

.. code:: python

	  import gym
	  env = gym.make('Humanoid')
	  env.reset()
	  env.render()

Toy text
--------

Toy environments which are text-based. There's no extra dependency to install, so to get started, you can just do:

.. code:: python

	  import gym
	  env = gym.make('FrozenLake')
	  env.reset()
	  env.render()

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 an simple random agent and upload the results to the scoreboard.
- 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) and upload the results to the scoreboard.
- Run `examples/scripts/list_envs <https://github.com/openai/gym/blob/master/examples/scripts/list_envs>`_ to generate a list of all environments. (You see also just `browse <https://gym.openai.com/docs>`_ the list on our site.
  - Run `examples/scripts/upload <https://github.com/openai/gym/blob/master/examples/scripts/upload>`_ to upload the recorded output from ``random_agent.py`` or ``cem.py``. Make sure to obtain an `API key <https://gym.openai.com/settings/profile>`_.

Testing
=======

We are using `nose2 <https://github.com/nose-devs/nose2>`_ for tests. You can run them via

.. code:: shell

	  nose2

You can also run tests in a specific directory by using the ``-s`` option, or by passing in the specific name of the test. See the `nose2 docs <http://nose2.readthedocs.org/en/latest/usage.html#naming-tests>`_ for more details.