139 lines
5.0 KiB
Python
Executable File
139 lines
5.0 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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"""Hopper domain."""
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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 collections
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from dm_control import mujoco
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from dm_control.rl import control
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from local_dm_control_suite import base
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from local_dm_control_suite import common
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from dm_control.suite.utils import randomizers
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from dm_control.utils import containers
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from dm_control.utils import rewards
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import numpy as np
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SUITE = containers.TaggedTasks()
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_CONTROL_TIMESTEP = .02 # (Seconds)
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# Default duration of an episode, in seconds.
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_DEFAULT_TIME_LIMIT = 20
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# Minimal height of torso over foot above which stand reward is 1.
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_STAND_HEIGHT = 0.6
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# Hopping speed above which hop reward is 1.
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_HOP_SPEED = 2
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def get_model_and_assets():
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"""Returns a tuple containing the model XML string and a dict of assets."""
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return common.read_model('hopper.xml'), common.ASSETS
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@SUITE.add('benchmarking')
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def stand(time_limit=_DEFAULT_TIME_LIMIT, random=None, environment_kwargs=None):
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"""Returns a Hopper that strives to stand upright, balancing its pose."""
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physics = Physics.from_xml_string(*get_model_and_assets())
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task = Hopper(hopping=False, random=random)
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environment_kwargs = environment_kwargs or {}
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return control.Environment(
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physics, task, time_limit=time_limit, control_timestep=_CONTROL_TIMESTEP,
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**environment_kwargs)
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@SUITE.add('benchmarking')
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def hop(time_limit=_DEFAULT_TIME_LIMIT, random=None, environment_kwargs=None):
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"""Returns a Hopper that strives to hop forward."""
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physics = Physics.from_xml_string(*get_model_and_assets())
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task = Hopper(hopping=True, random=random)
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environment_kwargs = environment_kwargs or {}
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return control.Environment(
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physics, task, time_limit=time_limit, control_timestep=_CONTROL_TIMESTEP,
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**environment_kwargs)
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class Physics(mujoco.Physics):
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"""Physics simulation with additional features for the Hopper domain."""
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def height(self):
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"""Returns height of torso with respect to foot."""
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return (self.named.data.xipos['torso', 'z'] -
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self.named.data.xipos['foot', 'z'])
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def speed(self):
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"""Returns horizontal speed of the Hopper."""
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return self.named.data.sensordata['torso_subtreelinvel'][0]
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def touch(self):
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"""Returns the signals from two foot touch sensors."""
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return np.log1p(self.named.data.sensordata[['touch_toe', 'touch_heel']])
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class Hopper(base.Task):
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"""A Hopper's `Task` to train a standing and a jumping Hopper."""
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def __init__(self, hopping, random=None):
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"""Initialize an instance of `Hopper`.
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Args:
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hopping: Boolean, if True the task is to hop forwards, otherwise it is to
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balance upright.
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random: Optional, either a `numpy.random.RandomState` instance, an
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integer 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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self._hopping = hopping
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super(Hopper, self).__init__(random=random)
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def initialize_episode(self, physics):
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"""Sets the state of the environment at the start of each episode."""
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randomizers.randomize_limited_and_rotational_joints(physics, self.random)
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self._timeout_progress = 0
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super(Hopper, self).initialize_episode(physics)
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def get_observation(self, physics):
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"""Returns an observation of positions, velocities and touch sensors."""
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obs = collections.OrderedDict()
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# Ignores horizontal position to maintain translational invariance:
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obs['position'] = physics.data.qpos[1:].copy()
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obs['velocity'] = physics.velocity()
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obs['touch'] = physics.touch()
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return obs
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def get_reward(self, physics):
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"""Returns a reward applicable to the performed task."""
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standing = rewards.tolerance(physics.height(), (_STAND_HEIGHT, 2))
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if self._hopping:
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hopping = rewards.tolerance(physics.speed(),
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bounds=(_HOP_SPEED, float('inf')),
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margin=_HOP_SPEED/2,
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value_at_margin=0.5,
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sigmoid='linear')
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return standing * hopping
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else:
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small_control = rewards.tolerance(physics.control(),
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margin=1, value_at_margin=0,
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sigmoid='quadratic').mean()
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small_control = (small_control + 4) / 5
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return standing * small_control
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