# 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.
# ============================================================================
"""Cheetah Domain."""
import collections
import os
from dm_control.suite import cheetah
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.utils import containers
from dm_control.utils import rewards
from dm_control.utils import io as resources
# How long the simulation will run, in seconds.
_DEFAULT_TIME_LIMIT = 10
_DOWN_HEIGHT = 0.15
_HIGH_HEIGHT = 1.00
_MID_HEIGHT = 0.45
# Running speed above which reward is 1.
_RUN_SPEED = 10
_SPIN_SPEED = 5
def make(task,
task_kwargs=None,
environment_kwargs=None,
visualize_reward=False):
task_kwargs = task_kwargs or {}
if environment_kwargs is not None:
task_kwargs = task_kwargs.copy()
task_kwargs['environment_kwargs'] = environment_kwargs
env = SUITE[task](**task_kwargs)
env.task.visualize_reward = visualize_reward
return env
def get_model_and_assets():
"""Returns a tuple containing the model XML string and a dict of assets."""
root_dir = os.path.dirname(os.path.dirname(__file__))
xml = resources.GetResource(
os.path.join(root_dir, 'custom_dmc_tasks', 'cheetah.xml'))
return xml, common.ASSETS
@cheetah.SUITE.add('custom')
def flipping(time_limit=_DEFAULT_TIME_LIMIT,
random=None,
environment_kwargs=None):
"""Returns the run task."""
physics = Physics.from_xml_string(*get_model_and_assets())
task = Cheetah(forward=False, flip=False, random=random, goal='flipping')
environment_kwargs = environment_kwargs or {}
return control.Environment(physics,
task,
time_limit=time_limit,
**environment_kwargs)
@cheetah.SUITE.add('custom')
def standing(time_limit=_DEFAULT_TIME_LIMIT,
random=None,
environment_kwargs=None):
"""Returns the run task."""
physics = Physics.from_xml_string(*get_model_and_assets())
task = Cheetah(forward=False, flip=False, random=random, goal='standing')
environment_kwargs = environment_kwargs or {}
return control.Environment(physics,
task,
time_limit=time_limit,
**environment_kwargs)
@cheetah.SUITE.add('custom')
def lying_down(time_limit=_DEFAULT_TIME_LIMIT,
random=None,
environment_kwargs=None):
"""Returns the run task."""
physics = Physics.from_xml_string(*get_model_and_assets())
task = Cheetah(forward=False, flip=False, random=random, goal='lying_down')
environment_kwargs = environment_kwargs or {}
return control.Environment(physics,
task,
time_limit=time_limit,
**environment_kwargs)
@cheetah.SUITE.add('custom')
def run_backward(time_limit=_DEFAULT_TIME_LIMIT,
random=None,
environment_kwargs=None):
"""Returns the run task."""
physics = Physics.from_xml_string(*get_model_and_assets())
task = Cheetah(forward=False, flip=False, random=random, goal='run_backward')
environment_kwargs = environment_kwargs or {}
return control.Environment(physics,
task,
time_limit=time_limit,
**environment_kwargs)
@cheetah.SUITE.add('custom')
def flip(time_limit=_DEFAULT_TIME_LIMIT,
random=None,
environment_kwargs=None):
"""Returns the run task."""
physics = Physics.from_xml_string(*get_model_and_assets())
task = Cheetah(forward=True, flip=True, random=random, goal='flip')
environment_kwargs = environment_kwargs or {}
return control.Environment(physics,
task,
time_limit=time_limit,
**environment_kwargs)
@cheetah.SUITE.add('custom')
def flip_backward(time_limit=_DEFAULT_TIME_LIMIT,
random=None,
environment_kwargs=None):
"""Returns the run task."""
physics = Physics.from_xml_string(*get_model_and_assets())
task = Cheetah(forward=False, flip=True, random=random, goal='flip_backward')
environment_kwargs = environment_kwargs or {}
return control.Environment(physics,
task,
time_limit=time_limit,
**environment_kwargs)
class Physics(mujoco.Physics):
"""Physics simulation with additional features for the Cheetah domain."""
def speed(self):
"""Returns the horizontal speed of the Cheetah."""
return self.named.data.sensordata['torso_subtreelinvel'][0]
def angmomentum(self):
"""Returns the angular momentum of torso of the Cheetah about Y axis."""
return self.named.data.subtree_angmom['torso'][1]
class Cheetah(base.Task):
"""A `Task` to train a running Cheetah."""
def __init__(self, goal=None, forward=True, flip=False, random=None):
self._forward = 1 if forward else -1
self._flip = flip
self._goal = goal
super(Cheetah, self).__init__(random=random)
def initialize_episode(self, physics):
"""Sets the state of the environment at the start of each episode."""
# The indexing below assumes that all joints have a single DOF.
assert physics.model.nq == physics.model.njnt
is_limited = physics.model.jnt_limited == 1
lower, upper = physics.model.jnt_range[is_limited].T
physics.data.qpos[is_limited] = self.random.uniform(lower, upper)
# Stabilize the model before the actual simulation.
for _ in range(200):
physics.step()
physics.data.time = 0
self._timeout_progress = 0
super().initialize_episode(physics)
def _get_lying_down_reward(self, physics):
torso = physics.named.data.xpos['torso', 'z']
torso_down = rewards.tolerance(torso,
bounds=(-float('inf'), _DOWN_HEIGHT),
margin=_DOWN_HEIGHT * 1.5,)
feet = physics.named.data.xpos['bfoot', 'z'] + physics.named.data.xpos['ffoot', 'z']
feet_up = rewards.tolerance(feet,
bounds=(_MID_HEIGHT, float('inf')),
margin=_MID_HEIGHT / 2,)
return (torso_down + feet_up) / 2
def _get_standing_reward(self, physics):
bfoot = physics.named.data.xpos['bfoot', 'z']
ffoot = physics.named.data.xpos['ffoot', 'z']
max_foot = bfoot if bfoot > ffoot else ffoot
min_foot = bfoot if bfoot <= ffoot else ffoot
low_foot_low = rewards.tolerance(min_foot,
bounds=(-float('inf'), _DOWN_HEIGHT),
margin=_DOWN_HEIGHT * 1.5,)
high_foot_high = rewards.tolerance(max_foot,
bounds=(_HIGH_HEIGHT, float('inf')),
margin=_HIGH_HEIGHT / 2,)
return high_foot_high * low_foot_low
def _get_flip_reward(self, physics):
return rewards.tolerance(self._forward * physics.angmomentum(),
bounds=(_SPIN_SPEED, float('inf')),
margin=_SPIN_SPEED,
value_at_margin=0,
sigmoid='linear')
def get_observation(self, physics):
"""Returns an observation of the state, ignoring horizontal position."""
obs = collections.OrderedDict()
# Ignores horizontal position to maintain translational invariance.
obs['position'] = physics.data.qpos[1:].copy()
obs['velocity'] = physics.velocity()
return obs
def get_reward(self, physics):
"""Returns a reward to the agent."""
if self._goal in ['run', 'flip', 'run_backward', 'flip_backward']:
if self._flip:
return self._get_flip_reward(physics)
else:
reward = rewards.tolerance(self._forward * physics.speed(),
bounds=(_RUN_SPEED, float('inf')),
margin=_RUN_SPEED,
value_at_margin=0,
sigmoid='linear')
return reward
elif self._goal == 'lying_down':
return self._get_lying_down_reward(physics)
elif self._goal == 'flipping':
self._forward = True
fwd_reward = self._get_flip_reward(physics)
self._forward = False
back_reward = self._get_flip_reward(physics)
return max(fwd_reward, back_reward)
elif self._goal == 'standing':
return self._get_standing_reward(physics)
else:
raise NotImplementedError(self._goal)