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- Title
A Study for Comparative Analysis of Dueling DQN and Centralized Critic Approaches in Multi-Agent Reinforcement Learning.
- Authors
Sugimoto, Masashi; Hasegawa, Kaito; Ishida, Yuuki; Ohnishi, Rikuto; Nakagami, Kouki; Tsuzuki, Shinji; Urushihara, Shiro; Sori, Hitoshi
- Abstract
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.
- Subjects
REINFORCEMENT learning; MULTIAGENT systems; INDUSTRIAL robots; COMPARATIVE studies; COMMUNICATIVE competence; AGGREGATION (Robotics)
- Publication
Journal of Robotics & Mechatronics, 2024, Vol 36, Issue 3, p589
- ISSN
0915-3942
- Publication type
Article
- DOI
10.20965/jrm.2024.p0589