A Hierarchical Reinforcement Learning Method for Persistent Time-Sensitive Tasks

Abstract

Reinforcement learning has been applied to many interesting problems such as the famous TD-gammon [1] and the inverted helicopter flight [2]. However little effort has been put into developing methods to learn policies for complex persistent tasks and tasks that are time-sensitive. In this paper we take a step towards solving this problem by using signal… (More)

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Cite this paper

@article{Li2016AHR, title={A Hierarchical Reinforcement Learning Method for Persistent Time-Sensitive Tasks}, author={Xiao Li and Calin Belta}, journal={CoRR}, year={2016}, volume={abs/1606.06355} }