On the Sample Efficiency of Abstractions and Potential-Based Reward Shaping in Reinforcement Learning

arXiv:2404.07826v1 Announce Type: new
Abstract: The use of Potential Based Reward Shaping (PBRS) has shown great promise in the ongoing research effort to tackle sample inefficiency in Reinforcement Learning (RL). However, the choice of the potential function is critical for this technique to be effective. Additionally, RL techniques are usually constrained to use a finite horizon for computational limitations. This introduces a bias when using PBRS, thus adding an additional layer of complexity. In this paper, we leverage abstractions to …

abstract abstractions arxiv cs.ai cs.lg efficiency function however reinforcement reinforcement learning research sample type