Multi-robot optimization control based on reinforcement learning is a research frontier of robotics and distributed
artificial intelligence in recent years. Some characteristics in multi-robot systems, such as distribution, heterogeneity
and high-dimensional continuity, lead to a series of challenges in theoretical and methodological research for multi-robot
reinforcement learning. Therefore, recent advances of multi-robot reinforcement learning are systematically surveyed.
Firstly, the fundamental theoretical models and optimization objectives are analyzed. Based on a contrastive analysis for
existing algorithms, the difficulties in theoretical research and implementations are discussed, and the possible solutions are
summarized in detail. Several benchmark problems and applications are listed. Finally, current work and future research
directions are concluded.