GENERATING EFFECTIVE INITIATION SETS FOR SUBGOAL-DRIVEN OPTIONS

Alper Demi̇r, Erki̇n Çi̇lden () and Faruk Polat ()
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Alper Demi̇r: Department of Computer Engineering, Middle East Technical University, 06531 Ankara, Turkey
Erki̇n Çi̇lden: #x2020;STM Defense Technologies Engineering and Trade Inc., 06530 Ankara, Turkey
Faruk Polat: Department of Computer Engineering, Middle East Technical University, 06531 Ankara, Turkey

Advances in Complex Systems (ACS), 2019, vol. 22, issue 02, 1-32

Abstract: Options framework is one of the prominent models serving as a basis to improve learning speed by means of temporal abstractions. An option is mainly composed of three elements: initiation set, option’s local policy and termination condition. Although various attempts exist that focus on how to derive high-quality termination conditions for a given problem, the impact of initiation set generation is relatively unexplored. In this work, we propose an effective goal-oriented heuristic method to derive useful initiation set elements via an analysis of the recent history of events. Effectiveness of the method is experimented on various benchmark problems, and the results are discussed.

Keywords: Reinforcement learning; options framework; option initiation set; subgoal discovery; Markov decision process (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0219525919500012

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