text: import random
  import numpy as np
  from gym import utils
  from gym.envs.mujoco import mujoco_env
  
  class HalfCheetahFANEnv(mujoco_env.MujocoEnv, utils.EzPickle):
      def __init__(self):
          self.reward_direction = 1 # True 
          mujoco_env.MujocoEnv.__init__(self, 'half_cheetah.xml', 5)
          utils.EzPickle.__init__(self)
  
      def step(self, action):
          xposbefore = self.sim.data.qpos[0]
          self.do_simulation(action, self.frame_skip)
          xposafter = self.sim.data.qpos[0]
          ob = self._get_obs()
          reward_ctrl = - 0.1 * np.square(action).sum()
  
          # NOTE: FAN change; modify by reward direction
          reward_run = self.reward_direction*(xposafter - xposbefore)/self.dt
  
          reward = reward_ctrl + reward_run
          done = False
          return ob, reward, done, dict(reward_run=reward_run, reward_ctrl=reward_ctrl)
  
      def _get_obs(self):
          return np.concatenate([
              self.sim.data.qpos.flat[1:],
              self.sim.data.qvel.flat,
          ])
  
      def reset_model(self):
  
          # NOTE: Set reward direction randomly
          self.reward_direction = random.choice([-1, 1])
  
          qpos = self.init_qpos + self.np_random.uniform(low=-.1, high=.1, size=self.model.nq)
          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