You can manage bookmarks using lists, please log in to your user account for this.
Media type:
E-Article
Title:
Resilient multi-agent RL: introducing DQ-RTS for distributed environments with data loss
Contributor:
Canese, Lorenzo;
Cardarilli, Gian Carlo;
Di Nunzio, Luca;
Fazzolari, Rocco;
Re, Marco;
Spanò, Sergio
Published:
Springer Science and Business Media LLC, 2024
Published in:
Scientific Reports, 14 (2024) 1
Language:
English
DOI:
10.1038/s41598-023-48767-1
ISSN:
2045-2322
Origination:
Footnote:
Description:
AbstractThis paper proposes DQ-RTS, a novel decentralized Multi-Agent Reinforcement Learning algorithm designed to address challenges posed by non-ideal communication and a varying number of agents in distributed environments. DQ-RTS incorporates an optimized communication protocol to mitigate data loss between agents. A comparative analysis between DQ-RTS and its decentralized counterpart Q-RTS, or Q-learning for Real-Time Swarms, demonstrates the superior convergence speed of DQ-RTS, achieving a remarkable speed-up factor ranging from 1.6 to 2.7 in scenarios with non-ideal communication. Moreover, DQ-RTS exhibits robustness by maintaining performance even when the agent population fluctuates, making it well-suited for applications requiring adaptable agent numbers over time. Additionally, extensive experiments conducted on various benchmark tasks validate the scalability and effectiveness of DQ-RTS, further establishing its potential as a practical solution for resilient Multi-Agent Reinforcement Learning in dynamic distributed environments.