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A History Tree Heuristic to Generate Better Initiation Sets for Options in Reinforcement Learning
Date
2016-09-02
Author
DEMİR, ALPER
Cilden, Erkin
Polat, Faruk
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Options framework is a prominent way to improve learning speed by means of temporally extended actions, called options. Although various attempts focusing on how to derive high quality termination conditions for options exist, the impact of initiation set generation of an option is relatively unexplored. In this work, we propose an effective heuristic method to derive useful initiation set elements via an analysis of the recent history of events.
URI
https://hdl.handle.net/11511/35681
DOI
https://doi.org/10.3233/978-1-61499-672-9-1644
Collections
Department of Computer Engineering, Conference / Seminar