"""Wrapper for rescaling actions to within a max and min action."""
from typing import Union
import numpy as np
import gymnasium as gym
from gymnasium.spaces import Box
[docs]class RescaleAction(gym.ActionWrapper, gym.utils.RecordConstructorArgs):
"""Affinely rescales the continuous action space of the environment to the range [min_action, max_action].
The base environment :attr:`env` must have an action space of type :class:`spaces.Box`. If :attr:`min_action`
or :attr:`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
"""
def __init__(
self,
env: gym.Env,
min_action: Union[float, int, np.ndarray],
max_action: Union[float, int, np.ndarray],
):
"""Initializes the :class:`RescaleAction` wrapper.
Args:
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.
"""
assert isinstance(
env.action_space, Box
), f"expected Box action space, got {type(env.action_space)}"
assert np.less_equal(min_action, max_action).all(), (min_action, max_action)
gym.utils.RecordConstructorArgs.__init__(
self, min_action=min_action, max_action=max_action
)
gym.ActionWrapper.__init__(self, env)
self.min_action = (
np.zeros(env.action_space.shape, dtype=env.action_space.dtype) + min_action
)
self.max_action = (
np.zeros(env.action_space.shape, dtype=env.action_space.dtype) + max_action
)
self.action_space = Box(
low=min_action,
high=max_action,
shape=env.action_space.shape,
dtype=env.action_space.dtype,
)
def action(self, action):
"""Rescales the action affinely from [:attr:`min_action`, :attr:`max_action`] to the action space of the base environment, :attr:`env`.
Args:
action: The action to rescale
Returns:
The rescaled action
"""
assert np.all(np.greater_equal(action, self.min_action)), (
action,
self.min_action,
)
assert np.all(np.less_equal(action, self.max_action)), (action, self.max_action)
low = self.env.action_space.low
high = self.env.action_space.high
action = low + (high - low) * (
(action - self.min_action) / (self.max_action - self.min_action)
)
action = np.clip(action, low, high)
return action
