Inverse Delayed Reinforcement Learning

Simon Sinong Zhan; Qingyuan Wu; Zhian Ruan; Frank Yang; Philip Wang; Yixuan Wang; Ruochen Jiao; Chao Huang; Qi Zhu

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arXiv · 2412.02931

Inverse Delayed Reinforcement Learning

Simon Sinong Zhan·Qingyuan Wu·Zhian Ruan·Frank Yang·Philip Wang·Yixuan Wang·Ruochen Jiao·Chao Huang·Qi Zhu

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Abstract

Inverse Reinforcement Learning (IRL) has demonstrated effectiveness in a variety of imitation tasks. In this paper, we introduce an IRL framework designed to extract rewarding features from expert trajectories affected by delayed disturbances. Instead of relying on direct observations, our approach employs an efficient off-policy adversarial training framework to derive expert features and recover optimal policies from augmented delayed observations. Empirical evaluations in the MuJoCo environment under diverse delay settings validate the effectiveness of our method. Furthermore, we provide a theoretical analysis showing that recovering expert policies from augmented delayed observations outperforms using direct delayed observations.

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BibTeX RIS

Simon Sinong Zhan, Qingyuan Wu, Zhian Ruan, Frank Yang, Philip Wang, Yixuan Wang, Ruochen Jiao, Chao Huang, Qi Zhu. 2024-12-04. Inverse Delayed Reinforcement Learning. https://arxiv.org/abs/2412.02931

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