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Learning by Automatic Option Discovery from Conditionally Terminating Sequences
Date
2006-08-28
Author
Girgin, Sertan
Polat, Faruk
Alhajj, Reda
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This paper proposes a novel approach to discover options in the form of conditionally terminating sequences, and shows how they can be integrated into reinforcement learning framework to improve the learning performance. The method utilizes stored histories of possible optimal policies and constructs a specialized tree structure online in order to identify action sequences which are used frequently together with states that are visited during the execution of such sequences. The tree is then used to implicitly run corresponding options. Effectiveness of the method is demonstrated empirically.
URI
https://hdl.handle.net/11511/53539
Collections
Department of Computer Engineering, Conference / Seminar
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S. Girgin, F. Polat, and R. Alhajj, “Learning by Automatic Option Discovery from Conditionally Terminating Sequences,” 2006, vol. 141, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53539.