def pre_process_reward(self, r):
        if self.clip_reward:
            r = np.clip(r,-self.clip_reward,self.clip_reward)
        if self.reward_rescale:
            self.r_max = max(np.amax(np.absolute(r)),self.r_max) 
            r = r / self.r_max
        return r
Clips and re-scales the rewards
dataset/ETHPy150Open sisl/Chimp/chimp/learners/dqn_learner.py/DQNLearner.pre_process_reward