Abstract
We study risk-sensitive reinforcement learning (RL), a crucial field due to its ability to enhance decision-making in scenarios where it is essential to manage uncertainty and minimize potential adverse outcomes.Particularly, our work focuses on applying the entropic risk measure to RL problems.While existing literature primarily investigates the online setting, there remains a large gap in understanding how to efficiently derive a near-optimal policy based on this risk measure using only a pre-collected dataset.We center on the linear Markov Decision Process (MDP) setting, a well-regarded theoretical framework that has yet to be examined from a risk-sensitive standpoint.In response, we introduce two provably sample-efficient algorithms.We begin by presenting a risk-sensitive pessimistic value iteration algorithm, offering a tight analysis by leveraging the structure of the risk-sensitive performance measure.To further improve the obtained bounds, we propose another pessimistic algorithm that utilizes variance information and reference-advantage decomposition, effectively improving both the dependence on the space dimension d and the risk-sensitivity factor.To the best of our knowledge, we obtain the first provably efficient risk-sensitive offline RL algorithms. Copyright 2024 by the author(s)
Original language | English |
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Title of host publication | Proceedings of the 41st International Conference on Machine Learning |
Editors | Ruslan Salakhutdinov, Zico Kolter, Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, Felix Berkenkamp |
Publisher | ML Research Press |
Pages | 59459-59489 |
Publication status | Published - Jul 2024 |
Externally published | Yes |
Event | 41st International Conference on Machine Learning (ICML 2024) - Messe Wien Exhibition Congress Center, Vienna, Austria Duration: 21 Jul 2024 → 27 Jul 2024 https://proceedings.mlr.press/v235/ https://icml.cc/ |
Publication series
Name | Proceedings of Machine Learning Research |
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Volume | 235 |
ISSN (Print) | 2640-3498 |
Conference
Conference | 41st International Conference on Machine Learning (ICML 2024) |
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Country/Territory | Austria |
City | Vienna |
Period | 21/07/24 → 27/07/24 |
Internet address |