text: import numpy as np
  from gym.spaces import  Box
  
  from metaworld.envs.asset_path_utils import full_v2_path_for
  from metaworld.envs.mujoco.sawyer_xyz.sawyer_xyz_env import SawyerXYZEnv, _assert_task_is_set
  
  
  class SawyerWindowCloseEnvV2(SawyerXYZEnv):
      """
      Motivation for V2:
          V1 was rarely solvable due to limited path length. The window usually
          only got ~25% closed before hitting max_path_length
      Changelog from V1 to V2:
          - (8/11/20) Updated to Byron's XML
          - (7/7/20) Added 3 element handle position to the observation
              (for consistency with other environments)
          - (6/15/20) Increased max_path_length from 150 to 200
      """
      def __init__(self):
  
          liftThresh = 0.02
          hand_low = (-0.5, 0.40, 0.05)
          hand_high = (0.5, 1, 0.5)
          obj_low = (0., 0.75, 0.2)
          obj_high = (0., 0.9, 0.2)
  
          super().__init__(
              self.model_name,
              hand_low=hand_low,
              hand_high=hand_high,
          )
  
          self.init_config = {
              'obj_init_angle': 0.3,
              'obj_init_pos': np.array([0, 0.785, 0.16], dtype=np.float32),
              'hand_init_pos': np.array([0, 0.4, 0.2], dtype=np.float32),
              'slider_init_pos': 0.2 # 0 - 0.2
          }
          self.obj_init_pos = self.init_config['obj_init_pos']
          self.obj_init_angle = self.init_config['obj_init_angle']
          self.hand_init_pos = self.init_config['hand_init_pos']
  
          goal_low = self.hand_low
          goal_high = self.hand_high
  
          self.max_path_length = 200
          self.liftThresh = liftThresh
  
          self._random_reset_space = Box(
              np.array(obj_low),
              np.array(obj_high),
          )
          self.goal_space = Box(np.array(goal_low), np.array(goal_high))
  
          self.maxPullDist = 0.2
          self.target_reward = 1000 * self.maxPullDist + 1000 * 2
  
      @property
      def model_name(self):
          return full_v2_path_for('sawyer_xyz/sawyer_window_horizontal.xml')
  
      @_assert_task_is_set
      def step(self, action):
          ob = super().step(action)
          reward, reachDist, pickrew, pullDist = self.compute_reward(action, ob)
          self.curr_path_length += 1
  
          info = {
              'reachDist': reachDist,
              'goalDist': pullDist,
              'epRew': reward,
              'pickRew': pickrew,
              'success': float(pullDist <= 0.05),
          }
  
          return ob, reward, False, info
  
      def _get_pos_objects(self):
          return self._get_site_pos('handleCloseStart')
  
      def reset_model(self):
          self._reset_hand()
  
          if self.random_init:
              self.obj_init_pos = self._get_state_rand_vec()
  
          self._target_pos = self.obj_init_pos.copy()
  
          self.sim.model.body_pos[self.model.body_name2id(
              'window'
          )] = self.obj_init_pos
  
          slider_init_pos = self.init_config['slider_init_pos']
          self.data.set_joint_qpos('window_slide', slider_init_pos)
          self.maxPullDist = slider_init_pos
          self.target_reward = 1000 * self.maxPullDist + 1000 * 2
  
          return self._get_obs()
  
      def _reset_hand(self):
          super()._reset_hand()
          self.reachCompleted = False
  
      def compute_reward(self, actions, obs):
          del actions
  
          objPos = obs[3:6]
  
          rightFinger, leftFinger = self._get_site_pos('rightEndEffector'), self._get_site_pos('leftEndEffector')
          fingerCOM  =  (rightFinger + leftFinger)/2
  
          pullGoal = self._target_pos
  
          pullDist = np.abs(objPos[0] - pullGoal[0])
          reachDist = np.linalg.norm(objPos - fingerCOM)
  
          self.reachCompleted = reachDist < 0.05
  
          c1 = 1000
          c2 = 0.01
          c3 = 0.001
          reachRew = -reachDist
  
          if self.reachCompleted:
              pullRew = 1000*(self.maxPullDist - pullDist) + c1*(np.exp(-(pullDist**2)/c2) + np.exp(-(pullDist**2)/c3))
          else:
              pullRew = 0
          reward = reachRew + pullRew
  
          return [reward, reachDist, None, pullDist]
  