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chore: update pytest
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# Copyright 2023 OmniSafe Team. All Rights Reserved. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
# ============================================================================== | ||
"""Interface of control barrier function-based environments.""" | ||
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# mypy: ignore-errors | ||
# pylint: disable=all | ||
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from __future__ import annotations | ||
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from typing import Any, ClassVar | ||
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import numpy as np | ||
import torch | ||
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from omnisafe.envs.classic_control.envs_from_rcbf import UnicycleEnv | ||
from omnisafe.envs.core import CMDP, env_register | ||
from omnisafe.typing import Box | ||
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@env_register | ||
class RobustBarrierFunctionEnv(CMDP): | ||
"""Interface of control barrier function-based environments. | ||
.. warning:: | ||
Since environments based on control barrier functions require special judgment and control | ||
of environmental dynamics, they do not support the use of vectorized environments for | ||
parallelization. | ||
Attributes: | ||
need_auto_reset_wrapper (bool): Whether to use auto reset wrapper. | ||
need_time_limit_wrapper (bool): Whether to use time limit wrapper. | ||
""" | ||
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need_auto_reset_wrapper = True | ||
need_time_limit_wrapper = False | ||
_support_envs: ClassVar[list[str]] = [ | ||
'Unicycle', | ||
] | ||
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def __init__( | ||
self, | ||
env_id: str, | ||
num_envs: int = 1, | ||
device: str = 'cpu', | ||
**kwargs: Any, | ||
) -> None: | ||
"""Initialize the environment. | ||
Args: | ||
env_id (str): Environment id. | ||
num_envs (int, optional): Number of environments. Defaults to 1. | ||
device (torch.device, optional): Device to store the data. Defaults to 'cpu'. | ||
Keyword Args: | ||
render_mode (str, optional): The render mode, ranging from ``human``, ``rgb_array``, ``rgb_array_list``. | ||
Defaults to ``rgb_array``. | ||
camera_name (str, optional): The camera name. | ||
camera_id (int, optional): The camera id. | ||
width (int, optional): The width of the rendered image. Defaults to 256. | ||
height (int, optional): The height of the rendered image. Defaults to 256. | ||
""" | ||
super().__init__(env_id) | ||
self._env_id = env_id | ||
if num_envs == 1: | ||
if self._env_id == 'Unicycle': | ||
self._env = UnicycleEnv() | ||
else: | ||
raise NotImplementedError('Only support Unicycle now.') | ||
assert isinstance(self._env.action_space, Box), 'Only support Box action space.' | ||
assert isinstance( | ||
self._env.observation_space, | ||
Box, | ||
), 'Only support Box observation space.' | ||
self._action_space = self._env.action_space | ||
self._observation_space = self._env.observation_space | ||
else: | ||
raise NotImplementedError('Only support num_envs=1 now.') | ||
self._device = torch.device(device) | ||
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self._num_envs = num_envs | ||
self._metadata = self._env.metadata | ||
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def step( | ||
self, | ||
action: torch.Tensor, | ||
) -> tuple[ | ||
torch.Tensor, | ||
torch.Tensor, | ||
torch.Tensor, | ||
torch.Tensor, | ||
torch.Tensor, | ||
dict[str, Any], | ||
]: | ||
"""Step the environment. | ||
.. note:: | ||
OmniSafe use auto reset wrapper to reset the environment when the episode is | ||
terminated. So the ``obs`` will be the first observation of the next episode. | ||
And the true ``final_observation`` in ``info`` will be stored in the ``final_observation`` key of ``info``. | ||
Args: | ||
action (torch.Tensor): Action to take. | ||
Returns: | ||
observation: Agent's observation of the current environment. | ||
reward: Amount of reward returned after previous action. | ||
cost: Amount of cost returned after previous action. | ||
terminated: Whether the episode has ended. | ||
truncated: Whether the episode has been truncated due to a time limit. | ||
info: Auxiliary diagnostic information (helpful for debugging, and sometimes learning). | ||
""" | ||
obs, reward, cost, terminated, truncated, info = self._env.step( | ||
action.detach().cpu().numpy(), | ||
) | ||
obs, reward, cost, terminated, truncated = ( | ||
torch.as_tensor(x, dtype=torch.float32, device=self._device) | ||
for x in (obs, reward, cost, terminated, truncated) | ||
) | ||
if 'final_observation' in info: | ||
info['final_observation'] = np.array( | ||
[ | ||
array if array is not None else np.zeros(obs.shape[-1]) | ||
for array in info['final_observation'] | ||
], | ||
) | ||
info['final_observation'] = torch.as_tensor( | ||
info['final_observation'], | ||
dtype=torch.float32, | ||
device=self._device, | ||
) | ||
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return obs, reward, cost, terminated, truncated, info | ||
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def reset( | ||
self, | ||
seed: int | None = None, | ||
options: dict[str, Any] | None = None, | ||
) -> tuple[torch.Tensor, dict]: | ||
"""Reset the environment. | ||
Args: | ||
seed (int, optional): The random seed. Defaults to None. | ||
options (dict[str, Any], optional): The options for the environment. Defaults to None. | ||
Returns: | ||
observation: Agent's observation of the current environment. | ||
info: Auxiliary diagnostic information (helpful for debugging, and sometimes learning). | ||
""" | ||
obs, info = self._env.reset(seed=seed, options=options) | ||
return torch.as_tensor(obs, dtype=torch.float32, device=self._device), info | ||
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def set_seed(self, seed: int) -> None: | ||
"""Set the seed for the environment. | ||
Args: | ||
seed (int): Seed to set. | ||
""" | ||
self.reset(seed=seed) | ||
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def render(self) -> Any: | ||
"""Render the environment. | ||
Returns: | ||
Rendered environment. | ||
""" | ||
return self._env.render() | ||
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def close(self) -> None: | ||
"""Close the environment.""" | ||
self._env.close() | ||
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def __getattr__(self, name: str) -> Any: | ||
"""Return the unwrapped environment attributes.""" | ||
return getattr(self._env, name) |
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