Resilient multi-agent RL: introducing DQ-RTS for distributed environments with data loss

Resilient multi-agent RL: introducing DQ-RTS for distributed environments with data loss

Abstract This 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...

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Bibliographic Details
Main Authors: Lorenzo Canese, Gian Carlo Cardarilli, Luca Di Nunzio, Rocco Fazzolari, Marco Re, Sergio Spanò
Format: Article
Language:English
Published: Nature Portfolio 2024-01-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-023-48767-1

Internet

https://doi.org/10.1038/s41598-023-48767-1

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