# Copyright 2017 The dm_control Authors.

#

# 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.

# ============================================================================

"""Base class for tasks in the Control Suite."""

from dm_control import mujoco

from dm_control.rl import control

import numpy as np

class Task(control.Task):

"""Base class for tasks in the Control Suite.

Actions are mapped directly to the states of MuJoCo actuators: each element of

the action array is used to set the control input for a single actuator. The

ordering of the actuators is the same as in the corresponding MJCF XML file.

Attributes:

random: A `numpy.random.RandomState` instance. This should be used to

generate all random variables associated with the task, such as random

starting states, observation noise* etc.

*If sensor noise is enabled in the MuJoCo model then this will be generated

using MuJoCo's internal RNG, which has its own independent state.

"""

def __init__(self, random=None):

"""Initializes a new continuous control task.

Args:

random: Optional, either a `numpy.random.RandomState` instance, an integer

seed for creating a new `RandomState`, or None to select a seed

automatically (default).

"""

if not isinstance(random, np.random.RandomState):

random = np.random.RandomState(random)

self._random = random

self._visualize_reward = False

@property

def random(self):

"""Task-specific `numpy.random.RandomState` instance."""

return self._random

def action_spec(self, physics):

"""Returns a `BoundedArraySpec` matching the `physics` actuators."""

return mujoco.action_spec(physics)

def initialize_episode(self, physics):

"""Resets geom colors to their defaults after starting a new episode.

Subclasses of `base.Task` must delegate to this method after performing

their own initialization.

Args:

physics: An instance of `mujoco.Physics`.

"""

self.after_step(physics)

def before_step(self, action, physics):

"""Sets the control signal for the actuators to values in `action`."""

# Support legacy internal code.

action = getattr(action, "continuous_actions", action)

physics.set_control(action)

def after_step(self, physics):

"""Modifies colors according to the reward."""

if self._visualize_reward:

reward = np.clip(self.get_reward(physics), 0.0, 1.0)

_set_reward_colors(physics, reward)

@property

def visualize_reward(self):

return self._visualize_reward

@visualize_reward.setter

def visualize_reward(self, value):

if not isinstance(value, bool):

raise ValueError("Expected a boolean, got {}.".format(type(value)))

self._visualize_reward = value

_MATERIALS = ["self", "effector", "target"]

_DEFAULT = [name + "_default" for name in _MATERIALS]

_HIGHLIGHT = [name + "_highlight" for name in _MATERIALS]

def _set_reward_colors(physics, reward):

"""Sets the highlight, effector and target colors according to the reward."""

assert 0.0 <= reward <= 1.0

colors = physics.named.model.mat_rgba

default = colors[_DEFAULT]

highlight = colors[_HIGHLIGHT]

blend_coef = reward ** 4 # Better color distinction near high rewards.

colors[_MATERIALS] = blend_coef * highlight + (1.0 - blend_coef) * default