231 lines
8.8 KiB
Python
Executable File
231 lines
8.8 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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"""Cartpole 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.utils import containers
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from dm_control.utils import rewards
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from lxml import etree
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import numpy as np
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from six.moves import range
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_DEFAULT_TIME_LIMIT = 10
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SUITE = containers.TaggedTasks()
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def get_model_and_assets(num_poles=1):
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"""Returns a tuple containing the model XML string and a dict of assets."""
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return _make_model(num_poles), common.ASSETS
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@SUITE.add('benchmarking')
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def balance(time_limit=_DEFAULT_TIME_LIMIT, random=None,
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environment_kwargs=None):
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"""Returns the Cartpole Balance task."""
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physics = Physics.from_xml_string(*get_model_and_assets())
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task = Balance(swing_up=False, sparse=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, **environment_kwargs)
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@SUITE.add('benchmarking')
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def balance_sparse(time_limit=_DEFAULT_TIME_LIMIT, random=None,
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environment_kwargs=None):
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"""Returns the sparse reward variant of the Cartpole Balance task."""
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physics = Physics.from_xml_string(*get_model_and_assets())
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task = Balance(swing_up=False, sparse=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, **environment_kwargs)
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@SUITE.add('benchmarking')
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def swingup(time_limit=_DEFAULT_TIME_LIMIT, random=None,
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environment_kwargs=None):
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"""Returns the Cartpole Swing-Up task."""
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physics = Physics.from_xml_string(*get_model_and_assets())
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task = Balance(swing_up=True, sparse=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, **environment_kwargs)
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@SUITE.add('benchmarking')
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def swingup_sparse(time_limit=_DEFAULT_TIME_LIMIT, random=None,
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environment_kwargs=None):
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"""Returns the sparse reward variant of teh Cartpole Swing-Up task."""
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physics = Physics.from_xml_string(*get_model_and_assets())
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task = Balance(swing_up=True, sparse=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, **environment_kwargs)
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@SUITE.add()
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def two_poles(time_limit=_DEFAULT_TIME_LIMIT, random=None,
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environment_kwargs=None):
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"""Returns the Cartpole Balance task with two poles."""
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physics = Physics.from_xml_string(*get_model_and_assets(num_poles=2))
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task = Balance(swing_up=True, sparse=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, **environment_kwargs)
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@SUITE.add()
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def three_poles(time_limit=_DEFAULT_TIME_LIMIT, random=None, num_poles=3,
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sparse=False, environment_kwargs=None):
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"""Returns the Cartpole Balance task with three or more poles."""
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physics = Physics.from_xml_string(*get_model_and_assets(num_poles=num_poles))
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task = Balance(swing_up=True, sparse=sparse, 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, **environment_kwargs)
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def _make_model(n_poles):
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"""Generates an xml string defining a cart with `n_poles` bodies."""
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xml_string = common.read_model('cartpole.xml')
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if n_poles == 1:
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return xml_string
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mjcf = etree.fromstring(xml_string)
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parent = mjcf.find('./worldbody/body/body') # Find first pole.
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# Make chain of poles.
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for pole_index in range(2, n_poles+1):
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child = etree.Element('body', name='pole_{}'.format(pole_index),
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pos='0 0 1', childclass='pole')
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etree.SubElement(child, 'joint', name='hinge_{}'.format(pole_index))
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etree.SubElement(child, 'geom', name='pole_{}'.format(pole_index))
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parent.append(child)
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parent = child
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# Move plane down.
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floor = mjcf.find('./worldbody/geom')
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floor.set('pos', '0 0 {}'.format(1 - n_poles - .05))
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# Move cameras back.
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cameras = mjcf.findall('./worldbody/camera')
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cameras[0].set('pos', '0 {} 1'.format(-1 - 2*n_poles))
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cameras[1].set('pos', '0 {} 2'.format(-2*n_poles))
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return etree.tostring(mjcf, pretty_print=True)
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class Physics(mujoco.Physics):
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"""Physics simulation with additional features for the Cartpole domain."""
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def cart_position(self):
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"""Returns the position of the cart."""
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return self.named.data.qpos['slider'][0]
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def angular_vel(self):
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"""Returns the angular velocity of the pole."""
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return self.data.qvel[1:]
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def pole_angle_cosine(self):
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"""Returns the cosine of the pole angle."""
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return self.named.data.xmat[2:, 'zz']
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def bounded_position(self):
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"""Returns the state, with pole angle split into sin/cos."""
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return np.hstack((self.cart_position(),
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self.named.data.xmat[2:, ['zz', 'xz']].ravel()))
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class Balance(base.Task):
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"""A Cartpole `Task` to balance the pole.
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State is initialized either close to the target configuration or at a random
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configuration.
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"""
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_CART_RANGE = (-.25, .25)
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_ANGLE_COSINE_RANGE = (.995, 1)
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def __init__(self, swing_up, sparse, random=None):
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"""Initializes an instance of `Balance`.
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Args:
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swing_up: A `bool`, which if `True` sets the cart to the middle of the
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slider and the pole pointing towards the ground. Otherwise, sets the
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cart to a random position on the slider and the pole to a random
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near-vertical position.
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sparse: A `bool`, whether to return a sparse or a smooth reward.
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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._sparse = sparse
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self._swing_up = swing_up
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super(Balance, 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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Initializes the cart and pole according to `swing_up`, and in both cases
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adds a small random initial velocity to break symmetry.
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Args:
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physics: An instance of `Physics`.
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"""
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nv = physics.model.nv
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if self._swing_up:
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physics.named.data.qpos['slider'] = .01*self.random.randn()
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physics.named.data.qpos['hinge_1'] = np.pi + .01*self.random.randn()
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physics.named.data.qpos[2:] = .1*self.random.randn(nv - 2)
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else:
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physics.named.data.qpos['slider'] = self.random.uniform(-.1, .1)
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physics.named.data.qpos[1:] = self.random.uniform(-.034, .034, nv - 1)
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physics.named.data.qvel[:] = 0.01 * self.random.randn(physics.model.nv)
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super(Balance, self).initialize_episode(physics)
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def get_observation(self, physics):
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"""Returns an observation of the (bounded) physics state."""
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obs = collections.OrderedDict()
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obs['position'] = physics.bounded_position()
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obs['velocity'] = physics.velocity()
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return obs
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def _get_reward(self, physics, sparse):
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if sparse:
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cart_in_bounds = rewards.tolerance(physics.cart_position(),
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self._CART_RANGE)
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angle_in_bounds = rewards.tolerance(physics.pole_angle_cosine(),
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self._ANGLE_COSINE_RANGE).prod()
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return cart_in_bounds * angle_in_bounds
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else:
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upright = (physics.pole_angle_cosine() + 1) / 2
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centered = rewards.tolerance(physics.cart_position(), margin=2)
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centered = (1 + centered) / 2
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small_control = rewards.tolerance(physics.control(), margin=1,
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value_at_margin=0,
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sigmoid='quadratic')[0]
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small_control = (4 + small_control) / 5
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small_velocity = rewards.tolerance(physics.angular_vel(), margin=5).min()
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small_velocity = (1 + small_velocity) / 2
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return upright.mean() * small_control * small_velocity * centered
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def get_reward(self, physics):
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"""Returns a sparse or a smooth reward, as specified in the constructor."""
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return self._get_reward(physics, sparse=self._sparse)
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