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Title: Non-stationary Reinforcement Learning under General Function Approximation
General function approximation is a powerful tool to handle large state and action spaces in a broad range of reinforcement learning (RL) scenarios. However, theoretical understanding of non-stationary MDPs with general function approximation is still limited. In this paper, we make the first such an attempt. We first propose a new complexity metric called dynamic Bellman Eluder (DBE) dimension for non-stationary MDPs, which subsumes majority of existing tractable RL problems in static MDPs as well as non-stationary MDPs. Based on the proposed complexity metric, we propose a novel confidence-set based model-free algorithm called SW-OPEA, which features a sliding window mechanism and a new confidence set design for non-stationary MDPs. We then establish an upper bound on the dynamic regret for the proposed algorithm, and show that SW-OPEA is provably efficient as long as the variation budget is not significantly large. We further demonstrate via examples of non-stationary linear and tabular MDPs that our algorithm performs better in small variation budget scenario than the existing UCB-type algorithms. To the best of our knowledge, this is the first dynamic regret analysis in non-stationary MDPs with general function approximation.  more » « less
Award ID(s):
2007117
NSF-PAR ID:
10466940
Author(s) / Creator(s):
; ; ; ; ;
Editor(s):
Krause, Andreas and
Publisher / Repository:
PMLR
Date Published:
Journal Name:
Proceedings of Machine Learning Research
Volume:
202
ISSN:
2640-3498
Page Range / eLocation ID:
9976--10007
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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