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JACIII Vol.26 No.5 pp. 706-714
doi: 10.20965/jaciii.2022.p0706
(2022)

Paper:

Proposal of Decision-Making Method Under Multi-Task Based on Q-Value Weighted by Task Priority

Tomomi Hanagata and Kentarou Kurashige

Muroran Institute of Technology
27-1 Mizumoto-cho, Muroran, Hokkaido 050-8585, Japan

Received:
December 20, 2021
Accepted:
April 30, 2022
Published:
September 20, 2022
Keywords:
reinforcement learning, multi-task, priority
Abstract
Proposal of Decision-Making Method Under Multi-Task Based on Q-Value Weighted by Task Priority

Euclidean distances for decision-making

Robots make decisions in a variety of situations requiring multitasking. Therefore, in this work, a method is studied to address multiple tasks based on reinforcement learning. Our previous method selects an action when the q-values of the action for each task correspond to a priority value in the q-table. However, the decision-making would select an ineffective action in particular situations. In this study, an action value weighted by priority is defined (termed as action priority) to indicate that the selected action is effective in accomplishing the task. Subsequently a method is proposed for selecting actions using action priorities. It is demonstrated that the proposed method can accomplish tasks faster with fewer errors.

Cite this article as:
T. Hanagata and K. Kurashige, “Proposal of Decision-Making Method Under Multi-Task Based on Q-Value Weighted by Task Priority,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.5, pp. 706-714, 2022.
Data files:
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Last updated on Sep. 22, 2022