Before revision:  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:  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 , across multiple domains, 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 .