113 lines
3.8 KiB
Python
Executable File
113 lines
3.8 KiB
Python
Executable File
# Copyright 2017 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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"""Base class for tasks in the Control Suite."""
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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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from dm_control import mujoco
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from dm_control.rl import control
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import numpy as np
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class Task(control.Task):
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"""Base class for tasks in the Control Suite.
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Actions are mapped directly to the states of MuJoCo actuators: each element of
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the action array is used to set the control input for a single actuator. The
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ordering of the actuators is the same as in the corresponding MJCF XML file.
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Attributes:
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random: A `numpy.random.RandomState` instance. This should be used to
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generate all random variables associated with the task, such as random
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starting states, observation noise* etc.
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*If sensor noise is enabled in the MuJoCo model then this will be generated
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using MuJoCo's internal RNG, which has its own independent state.
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"""
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def __init__(self, random=None):
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"""Initializes a new continuous control task.
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Args:
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random: Optional, either a `numpy.random.RandomState` instance, an integer
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seed for creating a new `RandomState`, or None to select a seed
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automatically (default).
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"""
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if not isinstance(random, np.random.RandomState):
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random = np.random.RandomState(random)
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self._random = random
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self._visualize_reward = False
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@property
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def random(self):
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"""Task-specific `numpy.random.RandomState` instance."""
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return self._random
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def action_spec(self, physics):
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"""Returns a `BoundedArraySpec` matching the `physics` actuators."""
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return mujoco.action_spec(physics)
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def initialize_episode(self, physics):
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"""Resets geom colors to their defaults after starting a new episode.
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Subclasses of `base.Task` must delegate to this method after performing
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their own initialization.
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Args:
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physics: An instance of `mujoco.Physics`.
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"""
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self.after_step(physics)
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def before_step(self, action, physics):
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"""Sets the control signal for the actuators to values in `action`."""
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# Support legacy internal code.
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action = getattr(action, "continuous_actions", action)
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physics.set_control(action)
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def after_step(self, physics):
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"""Modifies colors according to the reward."""
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if self._visualize_reward:
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reward = np.clip(self.get_reward(physics), 0.0, 1.0)
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_set_reward_colors(physics, reward)
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@property
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def visualize_reward(self):
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return self._visualize_reward
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@visualize_reward.setter
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def visualize_reward(self, value):
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if not isinstance(value, bool):
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raise ValueError("Expected a boolean, got {}.".format(type(value)))
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self._visualize_reward = value
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_MATERIALS = ["self", "effector", "target"]
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_DEFAULT = [name + "_default" for name in _MATERIALS]
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_HIGHLIGHT = [name + "_highlight" for name in _MATERIALS]
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def _set_reward_colors(physics, reward):
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"""Sets the highlight, effector and target colors according to the reward."""
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assert 0.0 <= reward <= 1.0
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colors = physics.named.model.mat_rgba
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default = colors[_DEFAULT]
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highlight = colors[_HIGHLIGHT]
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blend_coef = reward ** 4 # Better color distinction near high rewards.
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colors[_MATERIALS] = blend_coef * highlight + (1.0 - blend_coef) * default
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