Action Wrappers#

Base Class#

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

Superclass of wrappers that can modify the action before env.step().

If you would like to apply a function to the action before passing it to the base environment, you can simply inherit from ActionWrapper and overwrite the method action() to implement that transformation. The transformation defined in that method must take values in the base environment’s action space. However, its domain might differ from the original action space. In that case, you need to specify the new action space of the wrapper by setting self.action_space in the __init__() method of your wrapper.

Among others, Gymnasium provides the action wrappers ClipAction and RescaleAction for clipping and rescaling actions.

Constructor for the action wrapper.

action(action: WrapperActType) → ActType#

Returns a modified action before env.step() is called.

Parameters:

action – The original step() actions

Returns:

The modified actions

Available Action Wrappers#

class gymnasium.wrappers.ClipAction(env: Env)#

Clip the continuous action within the valid Box observation space bound.

Example

>>> import gymnasium as gym
>>> from gymnasium.wrappers import ClipAction
>>> env = gym.make("Hopper-v4")
>>> env = ClipAction(env)
>>> env.action_space
Box(-1.0, 1.0, (3,), float32)
>>> _ = env.reset(seed=42)
>>> _ = env.step(np.array([5.0, -2.0, 0.0]))
... # Executes the action np.array([1.0, -1.0, 0]) in the base environment

A wrapper for clipping continuous actions within the valid bound.

Parameters:

env – The environment to apply the wrapper

class gymnasium.wrappers.RescaleAction(env: Env, min_action: Union[float, int, ndarray], max_action: Union[float, int, ndarray])#

Affinely rescales the continuous action space of the environment to the range [min_action, max_action].

The base environment env must have an action space of type spaces.Box. If min_action or max_action are numpy arrays, the shape must match the shape of the environment’s action space.

Example

>>> import gymnasium as gym
>>> from gymnasium.wrappers import RescaleAction
>>> import numpy as np
>>> env = gym.make("Hopper-v4")
>>> _ = env.reset(seed=42)
>>> obs, _, _, _, _ = env.step(np.array([1,1,1]))
>>> _ = env.reset(seed=42)
>>> min_action = -0.5
>>> max_action = np.array([0.0, 0.5, 0.75])
>>> wrapped_env = RescaleAction(env, min_action=min_action, max_action=max_action)
>>> wrapped_env_obs, _, _, _, _ = wrapped_env.step(max_action)
>>> np.alltrue(obs == wrapped_env_obs)
True

Initializes the RescaleAction wrapper.

Parameters:

  • env (Env) – The environment to apply the wrapper

  • min_action (float, int or np.ndarray) – The min values for each action. This may be a numpy array or a scalar.

  • max_action (float, int or np.ndarray) – The max values for each action. This may be a numpy array or a scalar.