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Title: Adaptive Discretization in Online Reinforcement Learning
Discretization-based approaches to solving online reinforcement learning problems are studied extensively on applications such as resource allocation and cache management. The two major questions in designing discretization-based algorithms are how to create the discretization and when to refine it. There are several experimental results investigating heuristic approaches to these questions but little theoretical treatment. In this paper, we provide a unified theoretical analysis of model-free and model-based, tree-based adaptive hierarchical partitioning methods for online reinforcement learning. We show how our algorithms take advantage of inherent problem structure by providing guarantees that scale with respect to the “zooming” instead of the ambient dimension, an instance-dependent quantity measuring the benignness of the optimal [Formula: see text] function. Many applications in computing systems and operations research require algorithms that compete on three facets: low sample complexity, mild storage requirements, and low computational burden for policy evaluation and training. Our algorithms are easily adapted to operating constraints, and our theory provides explicit bounds across each of the three facets. Funding: This work is supported by funding from the National Science Foundation [Grants ECCS-1847393, DMS-1839346, CCF-1948256, and CNS-1955997] and the Army Research Laboratory [Grant W911NF-17-1-0094]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2396 .  more » « less
Award ID(s):
1955997 1948256 1847393
NSF-PAR ID:
10437343
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Operations Research
ISSN:
0030-364X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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