Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping

• URL: http://arxiv.org/abs/2312.09983v2
• Date: Mon, 18 Dec 2023 14:05:56 GMT
• Title: Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping
• Authors: Lauren H. Cooke, Harvey Klyne, Edwin Zhang, Cassidy Laidlaw, Milind Tambe, Finale Doshi-Velez
• Abstract summary: 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.
• Score: 42.09724642733125
• License: http://creativecommons.org/licenses/by/4.0/
• 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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