text: import numpy as np
  from gym import utils
  from gym.envs.mujoco import mujoco_env
  
  
  DEFAULT_CAMERA_CONFIG = {
      'distance': 4.0,
  }
  
  
  class AntEnv(mujoco_env.MujocoEnv, utils.EzPickle):
      def __init__(self,
                   xml_file='ant.xml',
                   ctrl_cost_weight=0.5,
                   contact_cost_weight=5e-4,
                   healthy_reward=1.0,
                   terminate_when_unhealthy=True,
                   healthy_z_range=(0.2, 1.0),
                   contact_force_range=(-1.0, 1.0),
                   reset_noise_scale=0.1,
                   exclude_current_positions_from_observation=True,
                   rgb_rendering_tracking=True):
          utils.EzPickle.__init__(**locals())
  
          self._ctrl_cost_weight = ctrl_cost_weight
          self._contact_cost_weight = contact_cost_weight
  
          self._healthy_reward = healthy_reward
          self._terminate_when_unhealthy = terminate_when_unhealthy
          self._healthy_z_range = healthy_z_range
  
          self._contact_force_range = contact_force_range
  
          self._reset_noise_scale = reset_noise_scale
  
          self._exclude_current_positions_from_observation = (
              exclude_current_positions_from_observation)
  
          mujoco_env.MujocoEnv.__init__(self, xml_file, 5, rgb_rendering_tracking=rgb_rendering_tracking)
  
      @property
      def healthy_reward(self):
          return float(
              self.is_healthy
              or self._terminate_when_unhealthy
          ) * self._healthy_reward
  
      def control_cost(self, action):
          control_cost = self._ctrl_cost_weight * np.sum(np.square(action))
          return control_cost
  
      @property
      def contact_forces(self):
          raw_contact_forces = self.sim.data.cfrc_ext
          min_value, max_value = self._contact_force_range
          contact_forces = np.clip(raw_contact_forces, min_value, max_value)
          return contact_forces
  
      @property
      def contact_cost(self):
          contact_cost = self._contact_cost_weight * np.sum(
              np.square(self.contact_forces))
          return contact_cost
  
      @property
      def is_healthy(self):
          state = self.state_vector()
          min_z, max_z = self._healthy_z_range
          is_healthy = (np.isfinite(state).all() and min_z <= state[2] <= max_z)
          return is_healthy
  
      @property
      def done(self):
          done = (not self.is_healthy
                  if self._terminate_when_unhealthy
                  else False)
          return done
  
      def step(self, action):
          xy_position_before = self.get_body_com("torso")[:2].copy()
          self.do_simulation(action, self.frame_skip)
          xy_position_after = self.get_body_com("torso")[:2].copy()
  
          xy_velocity = (xy_position_after - xy_position_before) / self.dt
          x_velocity, y_velocity = xy_velocity
  
          ctrl_cost = self.control_cost(action)
          contact_cost = self.contact_cost
  
          forward_reward = x_velocity
          healthy_reward = self.healthy_reward
  
          rewards = forward_reward + healthy_reward
          costs = ctrl_cost + contact_cost
  
          reward = rewards - costs
          done = self.done
          observation = self._get_obs()
          info = {
              'reward_forward': forward_reward,
              'reward_ctrl': -ctrl_cost,
              'reward_contact': -contact_cost,
              'reward_survive': healthy_reward,
  
              'x_position': xy_position_after[0],
              'y_position': xy_position_after[1],
              'distance_from_origin': np.linalg.norm(xy_position_after, ord=2),
  
              'x_velocity': x_velocity,
              'y_velocity': y_velocity,
              'forward_reward': forward_reward,
          }
  
          return observation, reward, done, info
  
      def _get_obs(self):
          position = self.sim.data.qpos.flat.copy()
          velocity = self.sim.data.qvel.flat.copy()
          contact_force = self.contact_forces.flat.copy()
  
          if self._exclude_current_positions_from_observation:
              position = position[2:]
  
          observations = np.concatenate((position, velocity, contact_force))
  
          return observations
  
      def reset_model(self):
          noise_low = -self._reset_noise_scale
          noise_high = self._reset_noise_scale
  
          qpos = self.init_qpos + self.np_random.uniform(
              low=noise_low, high=noise_high, size=self.model.nq)
          qvel = self.init_qvel + self._reset_noise_scale * self.np_random.randn(
              self.model.nv)
          self.set_state(qpos, qvel)
  
          observation = self._get_obs()
  
          return observation
  
      def viewer_setup(self):
          for key, value in DEFAULT_CAMERA_CONFIG.items():
              if isinstance(value, np.ndarray):
                  getattr(self.viewer.cam, key)[:] = value
              else:
                  setattr(self.viewer.cam, key, value)
  