Decision Making Under Multi Task Based on Priority for Each Task
In recent years, autonomous robots become to be desired to treat multi-task. A robot must decide a concrete action for plural objectives. Major researches try to realize this by weighted rewards. Weighted rewards can represent a human's intention easily. But weight of each task must change dynamically by a change of surrounding situation or of a robot status. Authors consider an independent learning for each task and selection of one concrete action from candidates of each learning. Authors propose a priority function to calculate priority for each task corresponding to surrounding situation or a robot status and propose a system which do decision making by using the priority function. Authors confirmed the usefulness of proposed method with simulation.
Year of publication: |
2016
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Authors: | Masaki, Takuya ; Kurashige, Kentarou |
Published in: |
International Journal of Artificial Life Research (IJALR). - IGI Global, ISSN 1947-3079, ZDB-ID 2696257-3. - Vol. 6.2016, 2 (01.07.), p. 88-98
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Publisher: |
IGI Global |
Subject: | Multi Task | Reinforcement Learning | Robots |
Saved in:
Online Resource
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