SEQUENTIAL LEARNING OF CONSTRAINTS FOR HIERARCHICAL REINFORCEMENT LEARNING

A computer-implemented method, computer program product, and computer processing system are provided for Hierarchical Reinforcement Learning (HRL) with a target task. The method includes obtaining, by a processor device, a sequence of tasks based on hierarchical relations between the tasks, the task...

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Bibliographic Details
Main Authors Agravante, Don Joven Ravoy, Pham, Tu-Hoa, De Magistris, Giovanni De, Tachibana, Ryuki
Format Patent
LanguageEnglish
Published 30.01.2020
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:A computer-implemented method, computer program product, and computer processing system are provided for Hierarchical Reinforcement Learning (HRL) with a target task. The method includes obtaining, by a processor device, a sequence of tasks based on hierarchical relations between the tasks, the tasks constituting the target task. The method further includes learning, by a processor device, a sequence of constraints corresponding to the sequence of tasks by repeating, for each of the tasks in the sequence, reinforcement learning and supervised learning with a set of good samples and a set of bad samples and by applying an obtained constraint for a current task to a next task.
Bibliography:Application Number: US201816048569