A Bayesian Approach to Robust Inverse Reinforcement Learning

Ran Wei, Siliang Zeng, Chenliang Li, Alfredo Garcia, Anthony McDonald, Mingyi Hong

Research output: Contribution to journalConference articlepeer-review

Abstract

We consider a Bayesian approach to offline model-based inverse reinforcement learning (IRL). The proposed framework differs from existing offline model-based IRL approaches by performing simultaneous estimation of the expert's reward function and subjective model of environment dynamics. We make use of a class of prior distributions which parameterizes how accurate the expert's model of the environment is to develop efficient algorithms to estimate the expert's reward and subjective dynamics in high-dimensional settings. Our analysis reveals a novel insight that the estimated policy exhibits robust performance when the expert is believed (a priori) to have a highly accurate model of the environment. We verify this observation in the MuJoCo environments and show that our algorithms outperform state-of-the-art offline IRL algorithms.

Original languageEnglish (US)
JournalProceedings of Machine Learning Research
Volume229
StatePublished - 2023
Event7th Conference on Robot Learning, CoRL 2023 - Atlanta, United States
Duration: Nov 6 2023Nov 9 2023

Bibliographical note

Publisher Copyright:
© 2023 Proceedings of Machine Learning Research. All Rights Reserved.

Keywords

  • Bayesian Inference
  • Inverse Reinforcement Learning
  • Robustness

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