Gymnasium/docs/api/env.md

---
title: Utils
---

# Env

## gymnasium.Env

```{eval-rst}
.. autoclass:: gymnasium.Env
```

### Methods

```{eval-rst}
.. autofunction:: gymnasium.Env.step
.. autofunction:: gymnasium.Env.reset
.. autofunction:: gymnasium.Env.render
```

### Attributes

```{eval-rst}
.. autoattribute:: gymnasium.Env.action_space

    The Space object corresponding to valid actions, all valid actions should be contained with the space. For example, if the action space is of type `Discrete` and gives the value `Discrete(2)`, this means there are two valid discrete actions: 0 & 1.

    .. code::

        >>> env.action_space
        Discrete(2)
        >>> env.observation_space
        Box(-3.4028234663852886e+38, 3.4028234663852886e+38, (4,), float32)

.. autoattribute:: gymnasium.Env.observation_space

    The Space object corresponding to valid observations, all valid observations should be contained with the space. For example, if the observation space is of type :class:`Box` and the shape of the object is ``(4,)``, this denotes a valid observation will be an array of 4 numbers. We can check the box bounds as well with attributes.

    .. code::

        >>> env.observation_space.high
        array([4.8000002e+00, 3.4028235e+38, 4.1887903e-01, 3.4028235e+38], dtype=float32)
        >>> env.observation_space.low
        array([-4.8000002e+00, -3.4028235e+38, -4.1887903e-01, -3.4028235e+38], dtype=float32)

.. autoattribute:: gymnasium.Env.metadata

    The metadata of the environment containing rendering modes, rendering fps, etc

.. autoattribute:: gymnasium.Env.render_mode

    The render mode of the environment determined at initialisation

.. autoattribute:: gymnasium.Env.reward_range

    A tuple corresponding to the minimum and maximum possible rewards for an agent over an episode. The default reward range is set to :math:`(-\infty,+\infty)`.

.. autoattribute:: gymnasium.Env.spec

    The ``EnvSpec`` of the environment normally set during :py:meth:`gymnasium.make`
```

### Additional Methods

```{eval-rst}
.. autofunction:: gymnasium.Env.close
.. autoproperty:: gymnasium.Env.unwrapped
.. autoproperty:: gymnasium.Env.np_random
```

### Implementing environments

```{eval-rst}
.. py:currentmodule:: gymnasium

When implementing an environment, the :meth:`Env.reset` and :meth:`Env.step` functions much be created describing the
dynamics of the environment.
For more information see the environment creation tutorial.
```