Before revision:  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 baseline, but also achieves a similar sample efficiency to MDPs that use hand-designed dense reward functions.
After revision:  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 MDPs that use hand-designed dense reward functions.