from typing import Any, Dict, List, SupportsFloat, Tuple, Union
import gymnasium as gym
import numpy as np
from packaging import version
[docs]class ContinuousToDiscrete(gym.ActionWrapper):
"""Gym environment wrapper to take discrete action in a continuous environment.
:param gym.Env env: gym environment with continuous action space.
:param int action_per_dim: number of discrete actions in each dimension
of the action space.
"""
def __init__(self, env: gym.Env, action_per_dim: Union[int, List[int]]) -> None:
super().__init__(env)
assert isinstance(env.action_space, gym.spaces.Box)
low, high = env.action_space.low, env.action_space.high
if isinstance(action_per_dim, int):
action_per_dim = [action_per_dim] * env.action_space.shape[0]
assert len(action_per_dim) == env.action_space.shape[0]
self.action_space = gym.spaces.MultiDiscrete(action_per_dim)
self.mesh = np.array(
[np.linspace(lo, hi, a) for lo, hi, a in zip(low, high, action_per_dim)],
dtype=object
)
[docs] def action(self, act: np.ndarray) -> np.ndarray: # type: ignore
# modify act
assert len(act.shape) <= 2, f"Unknown action format with shape {act.shape}."
if len(act.shape) == 1:
return np.array([self.mesh[i][a] for i, a in enumerate(act)])
return np.array([[self.mesh[i][a] for i, a in enumerate(a_)] for a_ in act])
class MultiDiscreteToDiscrete(gym.ActionWrapper):
"""Gym environment wrapper to take discrete action in multidiscrete environment.
:param gym.Env env: gym environment with multidiscrete action space.
"""
def __init__(self, env: gym.Env) -> None:
super().__init__(env)
assert isinstance(env.action_space, gym.spaces.MultiDiscrete)
nvec = env.action_space.nvec
assert nvec.ndim == 1
self.bases = np.ones_like(nvec)
for i in range(1, len(self.bases)):
self.bases[i] = self.bases[i - 1] * nvec[-i]
self.action_space = gym.spaces.Discrete(np.prod(nvec))
def action(self, act: np.ndarray) -> np.ndarray: # type: ignore
converted_act = []
for b in np.flip(self.bases):
converted_act.append(act // b)
act = act % b
return np.array(converted_act).transpose()
class TruncatedAsTerminated(gym.Wrapper):
"""A wrapper that set ``terminated = terminated or truncated`` for ``step()``.
It's intended to use with ``gym.wrappers.TimeLimit``.
:param gym.Env env: gym environment.
"""
def __init__(self, env: gym.Env):
super().__init__(env)
if not version.parse(gym.__version__) >= version.parse('0.26.0'):
raise EnvironmentError(
f"TruncatedAsTerminated is not applicable with gym version \
{gym.__version__}"
)
def step(self,
act: np.ndarray) -> Tuple[Any, SupportsFloat, bool, bool, Dict[str, Any]]:
observation, reward, terminated, truncated, info = super().step(act)
terminated = (terminated or truncated)
return observation, reward, terminated, truncated, info