Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping

Lauren H. Cooke, Harvey Klyne, Edwin Zhang, Cassidy Laidlaw, Milind Tambe, Finale Doshi-Velez

Keywords: Inverse Reinforcement Learning, Reward Shaping, Computational Efficiency

TL;DR: We propose the usage of potential-based reward shaping to reduce the computational burden of IRL.

Abstract: Inverse reinforcement learning (IRL) is computationally challenging, with common approaches requiring the solution of multiple reinforcement learning (RL) sub-problems. This work motivates the use of potential-based reward shaping to reduce the computational burden of each RL sub-problem. This work serves as a proof-of-concept and we hope will inspire future developments towards computationally efficient IRL.

Submission Number: 165