75 lines
2.7 KiB
Python
75 lines
2.7 KiB
Python
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# Copyright 2018 The dm_control Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Wrapper control suite environments that adds Gaussian noise to actions."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import dm_env
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import numpy as np
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_BOUNDS_MUST_BE_FINITE = (
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'All bounds in `env.action_spec()` must be finite, got: {action_spec}')
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class Wrapper(dm_env.Environment):
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"""Wraps a control environment and adds Gaussian noise to actions."""
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def __init__(self, env, scale=0.01):
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"""Initializes a new action noise Wrapper.
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Args:
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env: The control suite environment to wrap.
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scale: The standard deviation of the noise, expressed as a fraction
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of the max-min range for each action dimension.
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Raises:
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ValueError: If any of the action dimensions of the wrapped environment are
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unbounded.
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"""
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action_spec = env.action_spec()
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if not (np.all(np.isfinite(action_spec.minimum)) and
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np.all(np.isfinite(action_spec.maximum))):
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raise ValueError(_BOUNDS_MUST_BE_FINITE.format(action_spec=action_spec))
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self._minimum = action_spec.minimum
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self._maximum = action_spec.maximum
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self._noise_std = scale * (action_spec.maximum - action_spec.minimum)
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self._env = env
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def step(self, action):
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noisy_action = action + self._env.task.random.normal(scale=self._noise_std)
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# Clip the noisy actions in place so that they fall within the bounds
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# specified by the `action_spec`. Note that MuJoCo implicitly clips out-of-
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# bounds control inputs, but we also clip here in case the actions do not
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# correspond directly to MuJoCo actuators, or if there are other wrapper
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# layers that expect the actions to be within bounds.
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np.clip(noisy_action, self._minimum, self._maximum, out=noisy_action)
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return self._env.step(noisy_action)
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def reset(self):
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return self._env.reset()
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def observation_spec(self):
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return self._env.observation_spec()
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def action_spec(self):
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return self._env.action_spec()
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def __getattr__(self, name):
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return getattr(self._env, name)
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