You can manage bookmarks using lists, please log in to your user account for this.
Media type:
E-Article
Title:
Multi-Agent Reinforcement Learning: A Review of Challenges and Applications
Contributor:
Canese, Lorenzo;
Cardarilli, Gian Carlo;
Di Nunzio, Luca;
Fazzolari, Rocco;
Giardino, Daniele;
Re, Marco;
Spanò, Sergio
Published:
MDPI AG, 2021
Published in:
Applied Sciences, 11 (2021) 11, Seite 4948
Language:
English
DOI:
10.3390/app11114948
ISSN:
2076-3417
Origination:
Footnote:
Description:
<jats:p>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.</jats:p>