Barrier Functions Inspired Reward Shaping enhances training efficiency and safety in RL.
This paper introduces SASR, a novel self-adaptive reward shaping method for reinforcement learning that leverages success rates derived from historical experience to enhance learning in environments with sparse rewards.
ORSO is a novel approach that accelerates reward design in reinforcement learning by framing it as an online model selection problem, efficiently identifying effective shaping reward functions without human intervention.