TY - GEN
T1 - A Cooperative Learning Method for Multi-Agent System with Different Input Resolutions
AU - Uwano, Fumito
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Number JP20K23326.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/9/6
Y1 - 2021/9/6
N2 - 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.
AB - 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.
KW - Abstraction
KW - Hetero Resolution
KW - Multi-Agent System
KW - Neural Network
KW - Reinforcement Learning
UR - http://www.scopus.com/inward/record.url?scp=85118451529&partnerID=8YFLogxK
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U2 - 10.1109/ISAMSR53229.2021.9567835
DO - 10.1109/ISAMSR53229.2021.9567835
M3 - Conference contribution
AN - SCOPUS:85118451529
T3 - Proceedings - ISAMSR 2021: 4th International Symposium on Agents, Multi-Agents Systems and Robotics
SP - 84
EP - 90
BT - Proceedings - ISAMSR 2021
A2 - Abd.Wahab, Mohd Helmy
A2 - Hafit, Hanayanti
A2 - Darman, Rozanawati
A2 - Jaafar, Nur Huda
A2 - Ali, Azliza Mohd
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Symposium on Agents, Multi-Agents Systems and Robotics, ISAMSR 2021
Y2 - 6 September 2021 through 8 September 2021
ER -