A Cooperative Learning Method for Multi-Agent System with Different Input Resolutions

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Multi-Agent Reinforcement Learning controls some agents to learn group action with cooperation each other. For example, AGVs in warehouse as the agents cooperate with others and put on and off the supplies to organize them. Though Multi-Agent Reinforcement Learning seems to make advantage to apply multi-robot and more domains, this method has some problems, in particular, it cannot consider the sensor resolution in real world problem. This paper addresses this problem as hetero informational problem, and discuss how to solve the problem by the topology and learning of the neural network of the deep reinforcement learning. Concretely, This paper employed Asynchronous Advantageous Actor-Critic (A3C) with some kinds of neural networks to discuss through two experimental cases, single and multi agent domains. This paper compared performance of agents with different number of hidden layers of neural networks in the single agent domain, and investigate the performance on the environment whose agents have different resolution each other in the multi-agent domain.

Original languageEnglish
Title of host publicationProceedings - ISAMSR 2021
Subtitle of host publication4th International Symposium on Agents, Multi-Agents Systems and Robotics
EditorsMohd Helmy Abd.Wahab, Hanayanti Hafit, Rozanawati Darman, Nur Huda Jaafar, Azliza Mohd Ali
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages84-90
Number of pages7
ISBN (Electronic)9781728166544
DOIs
Publication statusPublished - Sep 6 2021
Event4th International Symposium on Agents, Multi-Agents Systems and Robotics, ISAMSR 2021 - Batu Pahat, Malaysia
Duration: Sep 6 2021Sep 8 2021

Publication series

NameProceedings - ISAMSR 2021: 4th International Symposium on Agents, Multi-Agents Systems and Robotics

Conference

Conference4th International Symposium on Agents, Multi-Agents Systems and Robotics, ISAMSR 2021
Country/TerritoryMalaysia
CityBatu Pahat
Period9/6/219/8/21

Keywords

  • Abstraction
  • Hetero Resolution
  • Multi-Agent System
  • Neural Network
  • Reinforcement Learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Optimization
  • Health Informatics

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