tia/Dreamer/local_dm_control_suite/fish.py

# 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.
# ============================================================================

"""Fish Domain."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections

from dm_control import mujoco
from dm_control.rl import control
from local_dm_control_suite import base
from local_dm_control_suite import common
from dm_control.utils import containers
from dm_control.utils import rewards
import numpy as np


_DEFAULT_TIME_LIMIT = 40
_CONTROL_TIMESTEP = .04
_JOINTS = ['tail1',
           'tail_twist',
           'tail2',
           'finright_roll',
           'finright_pitch',
           'finleft_roll',
           'finleft_pitch']
SUITE = containers.TaggedTasks()


def get_model_and_assets():
  """Returns a tuple containing the model XML string and a dict of assets."""
  return common.read_model('fish.xml'), common.ASSETS


@SUITE.add('benchmarking')
def upright(time_limit=_DEFAULT_TIME_LIMIT, random=None,
            environment_kwargs=None):
  """Returns the Fish Upright task."""
  physics = Physics.from_xml_string(*get_model_and_assets())
  task = Upright(random=random)
  environment_kwargs = environment_kwargs or {}
  return control.Environment(
      physics, task, control_timestep=_CONTROL_TIMESTEP, time_limit=time_limit,
      **environment_kwargs)


@SUITE.add('benchmarking')
def swim(time_limit=_DEFAULT_TIME_LIMIT, random=None, environment_kwargs=None):
  """Returns the Fish Swim task."""
  physics = Physics.from_xml_string(*get_model_and_assets())
  task = Swim(random=random)
  environment_kwargs = environment_kwargs or {}
  return control.Environment(
      physics, task, control_timestep=_CONTROL_TIMESTEP, time_limit=time_limit,
      **environment_kwargs)


class Physics(mujoco.Physics):
  """Physics simulation with additional features for the Fish domain."""

  def upright(self):
    """Returns projection from z-axes of torso to the z-axes of worldbody."""
    return self.named.data.xmat['torso', 'zz']

  def torso_velocity(self):
    """Returns velocities and angular velocities of the torso."""
    return self.data.sensordata

  def joint_velocities(self):
    """Returns the joint velocities."""
    return self.named.data.qvel[_JOINTS]

  def joint_angles(self):
    """Returns the joint positions."""
    return self.named.data.qpos[_JOINTS]

  def mouth_to_target(self):
    """Returns a vector, from mouth to target in local coordinate of mouth."""
    data = self.named.data
    mouth_to_target_global = data.geom_xpos['target'] - data.geom_xpos['mouth']
    return mouth_to_target_global.dot(data.geom_xmat['mouth'].reshape(3, 3))


class Upright(base.Task):
  """A Fish `Task` for getting the torso upright with smooth reward."""

  def __init__(self, random=None):
    """Initializes an instance of `Upright`.

    Args:
      random: Either an existing `numpy.random.RandomState` instance, an
        integer seed for creating a new `RandomState`, or None to select a seed
        automatically.
    """
    super(Upright, self).__init__(random=random)

  def initialize_episode(self, physics):
    """Randomizes the tail and fin angles and the orientation of the Fish."""
    quat = self.random.randn(4)
    physics.named.data.qpos['root'][3:7] = quat / np.linalg.norm(quat)
    for joint in _JOINTS:
      physics.named.data.qpos[joint] = self.random.uniform(-.2, .2)
    # Hide the target. It's irrelevant for this task.
    physics.named.model.geom_rgba['target', 3] = 0
    super(Upright, self).initialize_episode(physics)

  def get_observation(self, physics):
    """Returns an observation of joint angles, velocities and uprightness."""
    obs = collections.OrderedDict()
    obs['joint_angles'] = physics.joint_angles()
    obs['upright'] = physics.upright()
    obs['velocity'] = physics.velocity()
    return obs

  def get_reward(self, physics):
    """Returns a smooth reward."""
    return rewards.tolerance(physics.upright(), bounds=(1, 1), margin=1)


class Swim(base.Task):
  """A Fish `Task` for swimming with smooth reward."""

  def __init__(self, random=None):
    """Initializes an instance of `Swim`.

    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).
    """
    super(Swim, self).__init__(random=random)

  def initialize_episode(self, physics):
    """Sets the state of the environment at the start of each episode."""

    quat = self.random.randn(4)
    physics.named.data.qpos['root'][3:7] = quat / np.linalg.norm(quat)
    for joint in _JOINTS:
      physics.named.data.qpos[joint] = self.random.uniform(-.2, .2)
    # Randomize target position.
    physics.named.model.geom_pos['target', 'x'] = self.random.uniform(-.4, .4)
    physics.named.model.geom_pos['target', 'y'] = self.random.uniform(-.4, .4)
    physics.named.model.geom_pos['target', 'z'] = self.random.uniform(.1, .3)
    super(Swim, self).initialize_episode(physics)

  def get_observation(self, physics):
    """Returns an observation of joints, target direction and velocities."""
    obs = collections.OrderedDict()
    obs['joint_angles'] = physics.joint_angles()
    obs['upright'] = physics.upright()
    obs['target'] = physics.mouth_to_target()
    obs['velocity'] = physics.velocity()
    return obs

  def get_reward(self, physics):
    """Returns a smooth reward."""
    radii = physics.named.model.geom_size[['mouth', 'target'], 0].sum()
    in_target = rewards.tolerance(np.linalg.norm(physics.mouth_to_target()),
                                  bounds=(0, radii), margin=2*radii)
    is_upright = 0.5 * (physics.upright() + 1)
    return (7*in_target + is_upright) / 8