JRM Vol.36 No.3 pp. 589-602
doi: 10.20965/jrm.2024.p0589


A Study for Comparative Analysis of Dueling DQN and Centralized Critic Approaches in Multi-Agent Reinforcement Learning

Masashi Sugimoto*1 ORCID Icon, Kaito Hasegawa*2 ORCID Icon, Yuuki Ishida*1, Rikuto Ohnishi*1, Kouki Nakagami*1 ORCID Icon, Shinji Tsuzuki*3, Shiro Urushihara*4, and Hitoshi Sori*5

*1Division of Computer Science and Engineering, Department of Engineering of Innovation, National Institute of Technology, Tomakomai College
443 Nishikioka, Tomakomai, Hokkaido 059-1275, Japan

*2Advanced Course of Engineering for Innovation, Division of Electronics and Information Engineering, National Institute of Technology, Tomakomai College
443 Nishikioka, Tomakomai, Hokkaido 059-1275, Japan

*3Department of Electrical and Electronic Engineering and Computer Science, Graduate School of Science and Engineering, Ehime University
3 Bunkyo-cho, Matsuyama, Ehime 790-8577, Japan

*4Department of Electrical and Computer Engineering, National Institute of Technology, Kagawa College
355 Chokushi-cho, Takamatsu, Kagawa 761-8058, Japan

*5Communication and Information Systems Program, Department of Integrated Science and Technology, National Institute of Technology, Tsuyama College
624-1 Numa, Tsuyama, Okayama 708-8509, Japan

September 25, 2023
March 29, 2024
June 20, 2024
reinforcement learning, dueling DQN (deep Q-network), low power wide area (LPWA), swarm robotics, multi-agent system

In this study, we introduce a deep Q-network agent utilizing a dueling architecture to refine the valuation of actions through separate estimations of the state-value and action-value functions, adapted to facilitate concurrent multi-agent operations within a shared environment. Inspired by the self-organized, decentralized cooperation observed in natural swarms, this study uniquely integrates a centralized mechanism, or a centralized critic. This enhances performance and coherence in decision-making within the multi-agent system. This hybrid approach enables agents to execute informed and optimized decisions by considering the actions of their counterparts while maintaining an element of collective and flexible task-information sharing, thereby presenting a groundbreaking framework for cooperation and information sharing in swarm robot systems. To augment the communication capabilities, we employ low-power wide-area networks, or Long Range (LoRa), which are characterized by their low power consumption and long-range communication abilities, facilitating the sharing of task information and reducing the load on individual robots. The aim is to leverage LoRa as a communication platform to construct a cooperative algorithm that enables efficient task-information sharing among groups. This can provide innovative solutions and promote effective cooperation and communication within multi-agent systems, with significant implications for industrial and exploratory robots. In conclusion, by integrating a centralized system into the proposed model, this approach successfully enhances the performance of multi-agent systems in real-world applications, offering a balanced synergy between decentralized flexibility and centralized control.

Architecture of the proposed system based on centralized-critic approach

Architecture of the proposed system based on centralized-critic approach

Cite this article as:
M. Sugimoto, K. Hasegawa, Y. Ishida, R. Ohnishi, K. Nakagami, S. Tsuzuki, S. Urushihara, and H. Sori, “A Study for Comparative Analysis of Dueling DQN and Centralized Critic Approaches in Multi-Agent Reinforcement Learning,” J. Robot. Mechatron., Vol.36 No.3, pp. 589-602, 2024.
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Last updated on Jul. 12, 2024