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Title: Offline Reinforcement Learning with Closed-Form Policy Improvement Operators
Behavior constrained policy optimization has been demonstrated to be a successful paradigm for tackling Offline Reinforcement Learning. By exploiting historical transitions, a policy is trained to maximize a learned value function while constrained by the behavior policy to avoid a significant distributional shift. In this paper, we propose our closed-form policy improvement operators. We make a novel observation that the behavior constraint naturally motivates the use of first-order Taylor approximation, leading to a linear approximation of the policy objective. Additionally, as practical datasets are usually collected by heterogeneous policies, we model the behavior policies as a Gaussian Mixture and overcome the induced optimization difficulties by leveraging the LogSumExp’s lower bound and Jensen’s Inequality, giving rise to a closed-form policy improvement operator. We instantiate both one-step and iterative offline RL algorithms with our novel policy improvement operators and empirically demonstrate their effectiveness over state-of-the-art algorithms on the standard D4RL benchmark. Our code is available at https://cfpi-icml23.github.io/.  more » « less
Award ID(s):
2007117
NSF-PAR ID:
10466939
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:
20485--20528
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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