Before revision: We propose a novel reinforcement learning framework that performs self-supervised online reward shaping, yielding faster, sample efficient performance in sparse-reward environments  .
After revision: We introduce Self-supervised Online Reward Shaping (SORS), which aims to improve the sample efficiency of any RL algorithm in sparse-reward environments .