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JACIII Vol.26 No.4 pp. 504-512
doi: 10.20965/jaciii.2022.p0504
(2022)

Paper:

Multi-Faceted Decision Making Using Multiple Reinforcement Learning to Reducing Wasteful Actions

Riku Narita and Kentarou Kurashige

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

Received:
December 17, 2021
Accepted:
March 24, 2022
Published:
July 20, 2022
Keywords:
multi-agent system, reinforcement learning, multi-agent reinforcement learning
Abstract
Multi-Faceted Decision Making Using Multiple Reinforcement Learning to Reducing Wasteful Actions

Outline of the proposed system

Reinforcement learning can lead to autonomous behavior depending on the environment. However, in complex and high-dimensional environments, such as real environments, a large number of trials are required for learning. In this paper, we propose a solution for the learning problem using local learning to select an action based on the surrounding environmental information. Simulation experiments were conducted using maze problems, pitfall problems, and environments with random agents. The actions that did not contribute to task accomplishment were compared between the proposed method and ordinary reinforcement learning method.

Cite this article as:
R. Narita and K. Kurashige, “Multi-Faceted Decision Making Using Multiple Reinforcement Learning to Reducing Wasteful Actions,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.4, pp. 504-512, 2022.
Data files:
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Last updated on Dec. 01, 2022