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Positive impact of state similarity on reinforcement learning performance
Date
2007-10-01
Author
Girgin, Sertan
Polat, Faruk
Alhaj, Reda
Metadata
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In this paper, we propose a novel approach to identify states with similar subpolicies and show how they can be integrated into the reinforcement learning framework to improve learning performance. The method utilizes a specialized tree structure to identify common action sequences of states, which are derived from possible optimal policies, and defines a similarity function between two states based on the number of such sequences. Using this similarity function, updates on the action-value function of a state are reflected onto all similar states. This allows experience that is acquired during learning to be applied to a broader context. The effectiveness of the method is demonstrated empirically.
Subject Keywords
Control and Systems Engineering
,
Human-Computer Interaction
,
Electrical and Electronic Engineering
,
Software
,
Information Systems
,
General Medicine
,
Computer Science Applications
URI
https://hdl.handle.net/11511/46977
Journal
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
DOI
https://doi.org/10.1109/tsmcb.2007.899419
Collections
Department of Computer Engineering, Article
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S. Girgin, F. Polat, and R. Alhaj, “Positive impact of state similarity on reinforcement learning performance,”
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
, pp. 1256–1270, 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/46977.