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arXiv · 2312.09983

Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping

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

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BibTeXRIS

Lauren H. Cooke, Harvey Klyne, Edwin Zhang, Cassidy Laidlaw, Milind Tambe, Finale Doshi-Velez. 2023-12-15. Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping. https://arxiv.org/abs/2312.09983

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