An efficient approach to model-based hierarchical reinforcement learning

We propose a model-based approach to hierarchical reinforcement learning that exploits shared knowledge and selective execution at different levels of abstraction, to efficiently solve large, complex problems. Our framework adopts a new transition dynamics learning algorithm that identifies the comm...

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Bibliographic Details
Main Authors: LI, Zhuoru, NARAYAN, Akshay, LEONG, Tze-Yun
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2017
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Online Access:https://ink.library.smu.edu.sg/sis_research/4398
https://ink.library.smu.edu.sg/context/sis_research/article/5401/viewcontent/14771_66644_1_PB.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:We propose a model-based approach to hierarchical reinforcement learning that exploits shared knowledge and selective execution at different levels of abstraction, to efficiently solve large, complex problems. Our framework adopts a new transition dynamics learning algorithm that identifies the common action-feature combinations of the subtasks, and evaluates the subtask execution choices through simulation. The framework is sample efficient, and tolerates uncertain and incomplete problem characterization of the subtasks. We test the framework on common benchmark problems and complex simulated robotic environments. It compares favorably against the stateof-the-art algorithms, and scales well in very large problems.