强化学习
计算机科学
人工智能
可观测性
水准点(测量)
机器学习
可扩展性
数学
大地测量学
应用数学
数据库
地理
作者
Lorenzo Canese,G.C. Cardarilli,Luca Di Nunzio,Rocco Fazzolari,Daniele Giardino,M. Re,Sergio Spanò
摘要
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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