Multi-Agent Reinforcement Learning: A Review of Challenges and Applications

In this review, we present an analysis of the most used multi-agent reinforcement learning algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the most critical issues that must be taken into account in their extension to multi-agent scenarios. The analyzed algo...

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Bibliographic Details
Published in:Applied sciences Vol. 11; no. 11; p. 4948
Main Authors: Canese, Lorenzo, Cardarilli, Gian Carlo, Di Nunzio, Luca, Fazzolari, Rocco, Giardino, Daniele, Re, Marco, Spanò, Sergio
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.06.2021
Subjects:
Algorithms
Artificial intelligence
Control theory
Machine learning
Markov analysis
multi-agent
reinforcement learning
swarm
ISSN:2076-3417, 2076-3417
Online Access:Get full text
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Summary:In this review, we present an analysis of the most used multi-agent reinforcement learning algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the most critical issues that must be taken into account in their extension to multi-agent scenarios. The analyzed algorithms were grouped according to their features. We present a detailed taxonomy of the main multi-agent approaches proposed in the literature, focusing on their related mathematical models. For each algorithm, we describe the possible application fields, while pointing out its pros and cons. The described multi-agent algorithms are compared in terms of the most important characteristics for multi-agent reinforcement learning applications—namely, nonstationarity, scalability, and observability. We also describe the most common benchmark environments used to evaluate the performances of the considered methods.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app11114948