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 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.

Automated summary

This review presents an analysis of the most commonly used multi-agent reinforcement learning algorithms, starting from single-agent algorithms and extending to multi-agent scenarios. The algorithms are grouped based on their features and a detailed taxonomy of main approaches proposed in literature is provided, focusing on mathematical models. Each algorithm is described in terms of possible application fields, pros and cons, and compared based on important characteristics such as nonstationarity, scalability, and observability. Benchmark environments used to evaluate the methods' performances are also discussed.