""" DeepMind Control Suite Wrapper directly sourced from: https://github.com/denisyarats/dmc2gym MIT License Copyright (c) 2020 Denis Yarats Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import gymnasium as gym try: from dm_env import specs except ImportError: specs = None try: # Suppress MuJoCo warning (dm_control uses absl logging). import absl.logging absl.logging.set_verbosity("error") from dm_control import suite except (ImportError, OSError): suite = None import numpy as np from ray.rllib.utils.annotations import DeveloperAPI, PublicAPI def _spec_to_box(spec): def extract_min_max(s): assert s.dtype == np.float64 or s.dtype == np.float32 dim = np.int_(np.prod(s.shape)) if type(s) is specs.Array: bound = np.inf * np.ones(dim, dtype=np.float32) return -bound, bound elif type(s) is specs.BoundedArray: zeros = np.zeros(dim, dtype=np.float32) return s.minimum + zeros, s.maximum + zeros mins, maxs = [], [] for s in spec: mn, mx = extract_min_max(s) mins.append(mn) maxs.append(mx) low = np.concatenate(mins, axis=0) high = np.concatenate(maxs, axis=0) assert low.shape == high.shape return gym.spaces.Box(low, high, dtype=np.float32) def _flatten_obs(obs): obs_pieces = [] for v in obs.values(): flat = np.array([v]) if np.isscalar(v) else v.ravel() obs_pieces.append(flat) return np.concatenate(obs_pieces, axis=0) @PublicAPI class DMCEnv(gym.Env): def __init__( self, domain_name, task_name, task_kwargs=None, visualize_reward=False, from_pixels=False, height=64, width=64, camera_id=0, frame_skip=2, environment_kwargs=None, channels_first=True, preprocess=True, ): self._from_pixels = from_pixels self._height = height self._width = width self._camera_id = camera_id self._frame_skip = frame_skip self._channels_first = channels_first self.preprocess = preprocess if specs is None: raise RuntimeError( ( "The `specs` module from `dm_env` was not imported. Make sure " "`dm_env` is installed and visible in the current python " "environment." ) ) if suite is None: raise RuntimeError( ( "The `suite` module from `dm_control` was not imported. Make " "sure `dm_control` is installed and visible in the current " "python enviornment." ) ) # create task self._env = suite.load( domain_name=domain_name, task_name=task_name, task_kwargs=task_kwargs, visualize_reward=visualize_reward, environment_kwargs=environment_kwargs, ) # true and normalized action spaces self._true_action_space = _spec_to_box([self._env.action_spec()]) self._norm_action_space = gym.spaces.Box( low=-1.0, high=1.0, shape=self._true_action_space.shape, dtype=np.float32 ) # create observation space if from_pixels: shape = [3, height, width] if channels_first else [height, width, 3] self._observation_space = gym.spaces.Box( low=0, high=255, shape=shape, dtype=np.uint8 ) if preprocess: self._observation_space = gym.spaces.Box( low=-0.5, high=0.5, shape=shape, dtype=np.float32 ) else: self._observation_space = _spec_to_box( self._env.observation_spec().values() ) self._state_space = _spec_to_box(self._env.observation_spec().values()) self.current_state = None def __getattr__(self, name): return getattr(self._env, name) def _get_obs(self, time_step): if self._from_pixels: obs = self.render( height=self._height, width=self._width, camera_id=self._camera_id ) if self._channels_first: obs = obs.transpose(2, 0, 1).copy() if self.preprocess: obs = obs / 255.0 - 0.5 else: obs = _flatten_obs(time_step.observation) return obs.astype(np.float32) def _convert_action(self, action): action = action.astype(np.float64) true_delta = self._true_action_space.high - self._true_action_space.low norm_delta = self._norm_action_space.high - self._norm_action_space.low action = (action - self._norm_action_space.low) / norm_delta action = action * true_delta + self._true_action_space.low action = action.astype(np.float32) return action @property def observation_space(self): return self._observation_space @property def state_space(self): return self._state_space @property def action_space(self): return self._norm_action_space def step(self, action): assert self._norm_action_space.contains(action) action = self._convert_action(action) assert self._true_action_space.contains(action) reward = 0.0 extra = {"internal_state": self._env.physics.get_state().copy()} terminated = truncated = False for _ in range(self._frame_skip): time_step = self._env.step(action) reward += time_step.reward or 0.0 terminated = False truncated = time_step.last() if terminated or truncated: break obs = self._get_obs(time_step) self.current_state = _flatten_obs(time_step.observation) extra["discount"] = time_step.discount return obs, reward, terminated, truncated, extra def reset(self, *, seed=None, options=None): time_step = self._env.reset() self.current_state = _flatten_obs(time_step.observation) obs = self._get_obs(time_step) return obs, {} def render(self, mode="rgb_array", height=None, width=None, camera_id=0): assert mode == "rgb_array", "only support for rgb_array mode" height = height or self._height width = width or self._width camera_id = camera_id or self._camera_id return self._env.physics.render(height=height, width=width, camera_id=camera_id) @DeveloperAPI class ActionClip(gym.ActionWrapper): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._low = -1.0 self._high = 1.0 self.action_space = gym.spaces.Box( self._low, self._high, self.action_space.shape, self.action_space.dtype, ) def action(self, action): return np.clip(action, self._low, self._high)