text: # python3
  # pylint: disable=g-bad-file-header
  # Copyright 2019 DeepMind Technologies Limited. All Rights Reserved.
  #
  # 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.
  # ============================================================================
  """A swing up experiment in Cartpole."""
  
  from typing import Optional
  
  from bsuite.environments import base
  from bsuite.environments import cartpole
  from bsuite.experiments.cartpole_swingup import sweep
  
  import dm_env
  from dm_env import specs
  import numpy as np
  
  
  class CartpoleSwingup(base.Environment):
    """A difficult 'swing up' version of the classic Cart Pole task.
  
    In this version of the problem the pole begins downwards, and the agent must
    swing the pole up in order to see reward. Unlike the typical cartpole task
    the agent must pay a cost for moving, which aggravates the explore-exploit
    tradedoff. Algorithms without 'deep exploration' will simply remain still.
    """
  
    def __init__(self,
                 height_threshold: float = 0.5,
                 theta_dot_threshold: float = 1.,
                 x_reward_threshold: float = 1.,
                 move_cost: float = 0.1,
                 x_threshold: float = 3.,
                 timescale: float = 0.01,
                 max_time: float = 10.,
                 init_range: float = 0.05,
                 seed: Optional[int] = None):
      # Setup.
      self._state = cartpole.CartpoleState(0, 0, 0, 0, 0)
      super().__init__()
      self._rng = np.random.RandomState(seed)
      self._init_fn = lambda: self._rng.uniform(low=-init_range, high=init_range)
  
      # Logging info
      self._raw_return = 0.
      self._total_upright = 0.
      self._best_episode = 0.
      self._episode_return = 0.
  
      # Reward/episode logic
      self._height_threshold = height_threshold
      self._theta_dot_threshold = theta_dot_threshold
      self._x_reward_threshold = x_reward_threshold
      self._move_cost = move_cost
      self._x_threshold = x_threshold
      self._timescale = timescale
      self._max_time = max_time
  
      # Problem config
      self._cartpole_config = cartpole.CartpoleConfig(
          mass_cart=1.,
          mass_pole=0.1,
          length=0.5,
          force_mag=10.,
          gravity=9.8,
      )
  
      # Public attributes.
      self.bsuite_num_episodes = sweep.NUM_EPISODES
  
    def reset(self):
      self._reset_next_step = False
      self._state = cartpole.CartpoleState(
          x=self._init_fn(),
          x_dot=self._init_fn(),
          theta=np.pi + self._init_fn(),
          theta_dot=self._init_fn(),
          time_elapsed=0.,
      )
      self._episode_return = 0.
      return dm_env.restart(self.observation)
  
    def step(self, action):
      if self._reset_next_step:
        return self.reset()
  
      self._state = cartpole.step_cartpole(
          action=action,
          timescale=self._timescale,
          state=self._state,
          config=self._cartpole_config,
      )
  
      # Rewards only when the pole is central and balanced
      is_upright = (np.cos(self._state.theta) > self._height_threshold
                    and np.abs(self._state.theta_dot) < self._theta_dot_threshold
                    and np.abs(self._state.x) < self._x_reward_threshold)
      reward = -1. * np.abs(action - 1) * self._move_cost
  
      if is_upright:
        reward += 1.
        self._total_upright += 1
      self._raw_return += reward
      self._episode_return += reward
  
      is_end_of_episode = (self._state.time_elapsed > self._max_time
                           or np.abs(self._state.x) > self._x_threshold)
      if is_end_of_episode:
        self._best_episode = max(self._episode_return, self._best_episode)
        self._reset_next_step = True
        return dm_env.termination(reward=reward, observation=self.observation)
      else:  # continuing transition.
        return dm_env.transition(reward=reward, observation=self.observation)
  
    def _step(self, action: int) -> dm_env.TimeStep:
      raise NotImplementedError('This environment implements its own auto-reset.')
  
    def _reset(self) -> dm_env.TimeStep:
      raise NotImplementedError('This environment implements its own auto-reset.')
  
    def action_spec(self):
      return specs.DiscreteArray(dtype=np.int, num_values=3, name='action')
  
    def observation_spec(self):
      return specs.Array(shape=(1, 8), dtype=np.float32, name='state')
  
    @property
    def observation(self) -> np.ndarray:
      """Approximately normalize output."""
      obs = np.zeros((1, 8), dtype=np.float32)
      obs[0, 0] = self._state.x / self._x_threshold
      obs[0, 1] = self._state.x_dot / self._x_threshold
      obs[0, 2] = np.sin(self._state.theta)
      obs[0, 3] = np.cos(self._state.theta)
      obs[0, 4] = self._state.theta_dot
      obs[0, 5] = self._state.time_elapsed / self._max_time
      obs[0, 6] = 1. if np.abs(self._state.x) < self._x_reward_threshold else -1.
      theta_dot = self._state.theta_dot
      obs[0, 7] = 1. if np.abs(theta_dot) < self._theta_dot_threshold else -1.
      return obs
  
    def bsuite_info(self):
      return dict(raw_return=self._raw_return,
                  total_upright=self._total_upright,
                  best_episode=self._best_episode)
  