Before revision:  The proposed framework is based on the theory that altering the reward function does not affect the optimal policy of the original MDP as long as certain relations between the altered and the original reward are maintained. We name the proposed framework ClAssification-based Reward Shaping (CaReS), since the altered reward is learned in a self-supervised manner using classifier-based reward inference . Experimental results on several sparse-reward environments demonstrate that  the proposed algorithm is not only significantly more sample efficient than the state-of-the-art reinforcement learning baseline but also achieves a similar sample efficiency to a baseline that uses hand-designed dense reward functions  .
After revision:  The proposed framework is based on the theory that altering the reward function does not affect the optimal policy of the original MDP , while potentially increasing learning speed significantly . Experimental results on several sparse-reward environments demonstrate that the proposed algorithm is not only significantly more sample efficient than the state-of-the-art reinforcement learning baseline but also achieves a similar sample efficiency to a baseline that uses hand-designed dense reward functions .