Gymnasium/docs/introduction/basic_usage.md

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Basic Usage

.. py:currentmodule:: gymnasium

Gymnasium is a project that provides an API for all single agent reinforcement learning environments, and includes implementations of common environments: cartpole, pendulum, mountain-car, mujoco, atari, and more.

The API contains four key functions: :meth:`make`, :meth:`Env.reset`, :meth:`Env.step` and :meth:`Env.render`, that this basic usage will introduce you to. At the core of Gymnasium is :class:`Env`, a high-level python class representing a markov decision process (MDP) from reinforcement learning theory (this is not a perfect reconstruction, and is missing several components of MDPs). Within gymnasium, environments (MDPs) are implemented as :class:`Env` classes, along with :class:`Wrapper`, provide helpful utilities to change actions passed to the environment and modified the observations, rewards, termination or truncations conditions passed back to the user.

Initializing Environments

.. py:currentmodule:: gymnasium

Initializing environments is very easy in Gymnasium and can be done via the :meth:`make` function:

import gymnasium as gym
env = gym.make('CartPole-v1')

.. py:currentmodule:: gymnasium

This will return an :class:`Env` for users to interact with. To see all environments you can create, use :meth:`pprint_registry`. Furthermore, :meth:`make` provides a number of additional arguments for specifying keywords to the environment, adding more or less wrappers, etc.

Interacting with the Environment

The classic "agent-environment loop" pictured below is simplified representation of reinforcement learning that Gymnasium implements.

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This loop is implemented using the following gymnasium code

import gymnasium as gym
env = gym.make("LunarLander-v2", render_mode="human")
observation, info = env.reset()

for _ in range(1000):
    action = env.action_space.sample()  # agent policy that uses the observation and info
    observation, reward, terminated, truncated, info = env.step(action)

    if terminated or truncated:
        observation, info = env.reset()

env.close()

The output should look something like this:

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Explaining the code

.. py:currentmodule:: gymnasium

First, an environment is created using :meth:`make` with an additional keyword ``"render_mode"`` that specifies how the environment should be visualised.

.. py:currentmodule:: gymnasium.Env

See :meth:`render` for details on the default meaning of different render modes. In this example, we use the ``"LunarLander"`` environment where the agent controls a spaceship that needs to land safely.

After initializing the environment, we :meth:`reset` the environment to get the first observation of the environment. For initializing the environment with a particular random seed or options (see environment documentation for possible values) use the ``seed`` or ``options`` parameters with :meth:`reset`.

Next, the agent performs an action in the environment, :meth:`step`, this can be imagined as moving a robot or pressing a button on a games' controller that causes a change within the environment. As a result, the agent receives a new observation from the updated environment along with a reward for taking the action. This reward could be for instance positive for destroying an enemy or a negative reward for moving into lava. One such action-observation exchange is referred to as a **timestep**.

However, after some timesteps, the environment may end, this is called the terminal state. For instance, the robot may have crashed, or the agent have succeeded in completing a task, the environment will need to stop as the agent cannot continue. In gymnasium, if the environment has terminated, this is returned by :meth:`step`. Similarly, we may also want the environment to end after a fixed number of timesteps, in this case, the environment issues a truncated signal. If either of ``terminated`` or ``truncated`` are ``True`` then :meth:`reset` should be called next to restart the environment.

Action and observation spaces

.. py:currentmodule:: gymnasium.Env

Every environment specifies the format of valid actions and observations with the :attr:`action_space` and :attr:`observation_space` attributes. This is helpful for both knowing the expected input and output of the environment as all valid actions and observation should be contained with the respective space.

In the example, we sampled random actions via ``env.action_space.sample()`` instead of using an agent policy, mapping observations to actions which users will want to make. See one of the agent tutorials for an example of creating and training an agent policy.

.. py:currentmodule:: gymnasium

Every environment should have the attributes :attr:`Env.action_space` and :attr:`Env.observation_space`, both of which should be instances of classes that inherit from :class:`spaces.Space`. Gymnasium has support for a majority of possible spaces users might need:

.. py:currentmodule:: gymnasium.spaces

- :class:`Box`: describes an n-dimensional continuous space. It's a bounded space where we can define the upper and lower
  limits which describe the valid values our observations can take.
- :class:`Discrete`: describes a discrete space where ``{0, 1, ..., n-1}`` are the possible values our observation or action can take.
  Values can be shifted to ``{a, a+1, ..., a+n-1}`` using an optional argument.
- :class:`Dict`: represents a dictionary of simple spaces.
- :class:`Tuple`: represents a tuple of simple spaces.
- :class:`MultiBinary`: creates an n-shape binary space. Argument n can be a number or a list of numbers.
- :class:`MultiDiscrete`: consists of a series of :class:`Discrete` action spaces with a different number of actions in each element.

For example usage of spaces, see their `documentation </api/spaces>`_ along with `utility functions </api/spaces/utils>`_. There are a couple of more niche spaces :class:`Graph`, :class:`Sequence` and :class:`Text`.

Modifying the environment

.. py:currentmodule:: gymnasium.wrappers

Wrappers are a convenient way to modify an existing environment without having to alter the underlying code directly. Using wrappers will allow you to avoid a lot of boilerplate code and make your environment more modular. Wrappers can also be chained to combine their effects. Most environments that are generated via ``gymnasium.make`` will already be wrapped by default using the :class:`TimeLimitV0`, :class:`OrderEnforcingV0` and :class:`PassiveEnvCheckerV0`.

In order to wrap an environment, you must first initialize a base environment. Then you can pass this environment along with (possibly optional) parameters to the wrapper's constructor:

>>> import gymnasium as gym
>>> from gymnasium.wrappers import FlattenObservation
>>> env = gym.make("CarRacing-v2")
>>> env.observation_space.shape
(96, 96, 3)
>>> wrapped_env = FlattenObservation(env)
>>> wrapped_env.observation_space.shape
(27648,)

.. py:currentmodule:: gymnasium.wrappers

Gymnasium already provides many commonly used wrappers for you. Some examples:

- :class:`TimeLimitV0`: Issue a truncated signal if a maximum number of timesteps has been exceeded (or the base environment has issued a truncated signal).
- :class:`ClipActionV0`: Clip the action such that it lies in the action space (of type `Box`).
- :class:`RescaleActionV0`: Rescale actions to lie in a specified interval
- :class:`TimeAwareObservationV0`: Add information about the index of timestep to observation. In some cases helpful to ensure that transitions are Markov.

For a full list of implemented wrappers in gymnasium, see wrappers.

.. py:currentmodule:: gymnasium.Env

If you have a wrapped environment, and you want to get the unwrapped environment underneath all the layers of wrappers (so that you can manually call a function or change some underlying aspect of the environment), you can use the :attr:`unwrapped` attribute. If the environment is already a base environment, the :attr:`unwrapped` attribute will just return itself.

>>> wrapped_env
<FlattenObservation<TimeLimit<OrderEnforcing<PassiveEnvChecker<CarRacing<CarRacing-v2>>>>>>
>>> wrapped_env.unwrapped
<gymnasium.envs.box2d.car_racing.CarRacing object at 0x7f04efcb8850>

More information

• Making a Custom environment using the Gymnasium API
• Training an agent to play blackjack
• Compatibility with OpenAI Gym