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. Importantly wrappers can be chained to combine their effects and most environments that are generated via gymnasium.make() will already be wrapped by default.

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 RescaleAction
>>> base_env = gym.make("Hopper-v4")
>>> base_env.action_space
Box(-1.0, 1.0, (3,), float32)
>>> wrapped_env = RescaleAction(base_env, min_action=0, max_action=1)
>>> wrapped_env.action_space
Box(0.0, 1.0, (3,), float32)

You can access the environment underneath the first wrapper by using the gymnasium.Wrapper.env attribute. As the gymnasium.Wrapper class inherits from gymnasium.Env then gymnasium.Wrapper.env can be another wrapper.

>>> wrapped_env
>>> wrapped_env.env

If you want to get to the environment underneath all of the layers of wrappers, you can use the gymnasium.Wrapper.unwrapped attribute. If the environment is already a bare environment, the gymnasium.Wrapper.unwrapped attribute will just return itself.

>>> wrapped_env
>>> wrapped_env.unwrapped 
<gymnasium.envs.mujoco.hopper_v4.HopperEnv object at 0x7fbb5efd0490>

There are three common things you might want a wrapper to do:

  • Transform actions before applying them to the base environment

  • Transform observations that are returned by the base environment

  • Transform rewards that are returned by the base environment

Such wrappers can be easily implemented by inheriting from gymnasium.ActionWrapper, gymnasium.ObservationWrapper, or gymnasium.RewardWrapper and implementing the respective transformation. If you need a wrapper to do more complicated tasks, you can inherit from the gymnasium.Wrapper class directly.

If you’d like to implement your own custom wrapper, check out the corresponding tutorial.

class gymnasium.Wrapper(env: Env[ObsType, ActType])[source]#

Wraps a gymnasium.Env to allow a modular transformation of the step() and reset() methods.

This class is the base class of all wrappers to change the behavior of the underlying environment. Wrappers that inherit from this class can modify the action_space, observation_space, reward_range and metadata attributes, without changing the underlying environment’s attributes. Moreover, the behavior of the step() and reset() methods can be changed by these wrappers.

Some attributes (spec, render_mode, np_random) will point back to the wrapper’s environment (i.e. to the corresponding attributes of env).


If you inherit from Wrapper, don’t forget to call super().__init__(env)


env – The environment to wrap


Wrapper.step(action: WrapperActType) tuple[WrapperObsType, SupportsFloat, bool, bool, dict[str, Any]][source]#

Uses the step() of the env that can be overwritten to change the returned data.

Wrapper.reset(*, seed: int | None = None, options: dict[str, Any] | None = None) tuple[WrapperObsType, dict[str, Any]][source]#

Uses the reset() of the env that can be overwritten to change the returned data.

Wrapper.render() RenderFrame | list[RenderFrame] | None[source]#

Uses the render() of the env that can be overwritten to change the returned data.


Closes the wrapper and env.

classmethod Wrapper.wrapper_spec(**kwargs: Any) WrapperSpec[source]#

Generates a WrapperSpec for the wrappers.

Wrapper.get_wrapper_attr(name: str) Any[source]#

Gets an attribute from the wrapper and lower environments if name doesn’t exist in this object.


name – The variable name to get


The variable with name in wrapper or lower environments

Wrapper.set_wrapper_attr(name: str, value: Any)[source]#

Sets an attribute on this wrapper or lower environment if name is already defined.

  • name – The variable name

  • value – The new variable value



The environment (one level underneath) this wrapper.

This may itself be a wrapped environment. To obtain the environment underneath all layers of wrappers, use gymnasium.Wrapper.unwrapped.

property Wrapper.action_space: spaces.Space[ActType] | spaces.Space[WrapperActType]#

Return the Env action_space unless overwritten then the wrapper action_space is used.

property Wrapper.observation_space: spaces.Space[ObsType] | spaces.Space[WrapperObsType]#

Return the Env observation_space unless overwritten then the wrapper observation_space is used.

property Wrapper.spec: EnvSpec | None#

Returns the Env spec attribute with the WrapperSpec if the wrapper inherits from EzPickle.

property Wrapper.metadata: dict[str, Any]#

Returns the Env metadata.

property Wrapper.np_random: Generator#

Returns the Env np_random attribute.

property Wrapper.np_random_seed: int | None#

Returns the base enviroment’s np_random_seed.

property Wrapper.unwrapped: Env[ObsType, ActType]#

Returns the base environment of the wrapper.

This will be the bare gymnasium.Env environment, underneath all layers of wrappers.