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