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| """Wrapper for rescaling actions to within a max and min action.""" | |
| from typing import Union | |
| import numpy as np | |
| import gym | |
| from gym import spaces | |
| class RescaleAction(gym.ActionWrapper): | |
| """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 gym | |
| >>> env = gym.make('BipedalWalker-v3') | |
| >>> env.action_space | |
| Box(-1.0, 1.0, (4,), float32) | |
| >>> min_action = -0.5 | |
| >>> max_action = np.array([0.0, 0.5, 1.0, 0.75]) | |
| >>> env = RescaleAction(env, min_action=min_action, max_action=max_action) | |
| >>> env.action_space | |
| Box(-0.5, [0. 0.5 1. 0.75], (4,), float32) | |
| >>> RescaleAction(env, min_action, max_action).action_space == gym.spaces.Box(min_action, max_action) | |
| 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, spaces.Box | |
| ), f"expected Box action space, got {type(env.action_space)}" | |
| assert np.less_equal(min_action, max_action).all(), (min_action, max_action) | |
| super().__init__(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 = spaces.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 | |