Positive impact of state similarity on reinforcement learning performance

Date

2007-10-01

Author

Girgin, Sertan
Polat, Faruk
Alhaj, Reda

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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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Citation Formats

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.