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Multi-Agent Reinforcement Learning: A Review of Challenges and Applications
Lorenzo Canese (University of Rome Tor Vergata), G.C. Cardarilli (University of Rome Tor Vergata), Luca Di Nunzio (University of Rome Tor Vergata), Rocco Fazzolari (University of Rome Tor Vergata), Daniele Giardino (University of Rome Tor Vergata), Marco Re (University of Rome Tor Vergata), Sergio Spanò (University of Rome Tor Vergata)
Abstract: 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.