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

"""Acrobot 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.utils import containers
from dm_control.utils import rewards
import numpy as np

_DEFAULT_TIME_LIMIT = 10
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('acrobot.xml'), common.ASSETS


@SUITE.add('benchmarking')
def swingup(time_limit=_DEFAULT_TIME_LIMIT, random=None,
            environment_kwargs=None):
  """Returns Acrobot balance task."""
  physics = Physics.from_xml_string(*get_model_and_assets())
  task = Balance(sparse=False, random=random)
  environment_kwargs = environment_kwargs or {}
  return control.Environment(
      physics, task, time_limit=time_limit, **environment_kwargs)


@SUITE.add('benchmarking')
def swingup_sparse(time_limit=_DEFAULT_TIME_LIMIT, random=None,
                   environment_kwargs=None):
  """Returns Acrobot sparse balance."""
  physics = Physics.from_xml_string(*get_model_and_assets())
  task = Balance(sparse=True, random=random)
  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 Acrobot domain."""

  def horizontal(self):
    """Returns horizontal (x) component of body frame z-axes."""
    return self.named.data.xmat[['upper_arm', 'lower_arm'], 'xz']

  def vertical(self):
    """Returns vertical (z) component of body frame z-axes."""
    return self.named.data.xmat[['upper_arm', 'lower_arm'], 'zz']

  def to_target(self):
    """Returns the distance from the tip to the target."""
    tip_to_target = (self.named.data.site_xpos['target'] -
                     self.named.data.site_xpos['tip'])
    return np.linalg.norm(tip_to_target)

  def orientations(self):
    """Returns the sines and cosines of the pole angles."""
    return np.concatenate((self.horizontal(), self.vertical()))


class Balance(base.Task):
  """An Acrobot `Task` to swing up and balance the pole."""

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

    Args:
      sparse: A `bool` specifying whether to use a sparse (indicator) reward.
      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._sparse = sparse
    super().__init__(random=random)

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

    Shoulder and elbow are set to a random position between [-pi, pi).

    Args:
      physics: An instance of `Physics`.
    """
    physics.named.data.qpos[
        ['shoulder', 'elbow']] = self.random.uniform(-np.pi, np.pi, 2)
    super().initialize_episode(physics)

  def get_observation(self, physics):
    """Returns an observation of pole orientation and angular velocities."""
    obs = collections.OrderedDict()
    obs['orientations'] = physics.orientations()
    obs['velocity'] = physics.velocity()
    return obs

  def _get_reward(self, physics, sparse):
    target_radius = physics.named.model.site_size['target', 0]
    return rewards.tolerance(physics.to_target(),
                             bounds=(0, target_radius),
                             margin=0 if sparse else 1)

  def get_reward(self, physics):
    """Returns a sparse or a smooth reward, as specified in the constructor."""
    return self._get_reward(physics, sparse=self._sparse)
