applied sciences Review Multi-Agent Reinforcement Learning: A Review of Challenges and Applications Lorenzo Canese †, Gian Carlo Cardarilli, Luca Di Nunzio †, Rocco Fazzolari † , Daniele Giardino †, Marco Re † and Sergio Spanò *,† Department of Electronic Engineering, University of Rome “Tor Vergata”, Via del Politecnico 1, 00133 Rome, Italy;
[email protected] (L.C.);
[email protected] (G.C.C.);
[email protected] (L.D.N.);
[email protected] (R.F.);
[email protected] (D.G.);
[email protected] (M.R.) * Correspondence:
[email protected]; Tel.: +39-06-7259-7273 † These authors contributed equally to this work. Abstract: In this review, we present an analysis of the most used multi-agent reinforcement learn- ing 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 char- acteristics for multi-agent reinforcement learning applications—namely, nonstationarity, scalability, and observability. We also describe the most common benchmark environments used to evaluate the Citation: Canese, L.; Cardarilli, G.C.; performances of the considered methods. Di Nunzio, L.; Fazzolari, R.; Giardino, D.; Re, M.; Spanò, S. Multi-Agent Keywords: machine learning; reinforcement learning; multi-agent; swarm Reinforcement Learning: A Review of Challenges and Applications.