text: import numpy as np
  from gym import utils
  from gym.envs.mujoco import mujoco_env
  
  class AntEnv(mujoco_env.MujocoEnv, utils.EzPickle):
      def __init__(self):
          mujoco_env.MujocoEnv.__init__(self, 'ant.xml', 5)
          utils.EzPickle.__init__(self)
  
      def _step(self, a):
          xposbefore = self.get_body_com("torso")[0]
          self.do_simulation(a, self.frame_skip)
          xposafter = self.get_body_com("torso")[0]
          forward_reward = (xposafter - xposbefore)/self.dt
          ctrl_cost = .5 * np.square(a).sum()
          contact_cost = 0.5 * 1e-3 * np.sum(
              np.square(np.clip(self.model.data.cfrc_ext, -1, 1)))
          survive_reward = 1.0
          reward = forward_reward - ctrl_cost - contact_cost + survive_reward
          state = self.state_vector()
          notdone = np.isfinite(state).all() \
              and state[2] >= 0.2 and state[2] <= 1.0
          done = not notdone
          ob = self._get_obs()
          return ob, reward, done, dict(
              reward_forward=forward_reward,
              reward_ctrl=-ctrl_cost,
              reward_contact=-contact_cost,
              reward_survive=survive_reward)
  
      def _get_obs(self):
          return np.concatenate([
              self.model.data.qpos.flat[2:],
              self.model.data.qvel.flat,
              np.clip(self.model.data.cfrc_ext, -1, 1).flat,
          ])
  
      def reset_model(self):
          qpos = self.init_qpos + self.np_random.uniform(size=self.model.nq, low=-.1, high=.1)
          qvel = self.init_qvel + self.np_random.randn(self.model.nv) * .1
          self.set_state(qpos, qvel)
          return self._get_obs()
  
      def viewer_setup(self):
          self.viewer.cam.distance = self.model.stat.extent * 0.5
  