Planning and learning in multi-agent path finding

Multi-agent path finding arises, on the one hand, in numerous applied areas. A classical example is automated warehouses with a large number of mobile goods-sorting robots operating simultaneously. On the other hand, for this problem, there are no universal solution methods that simultaneously satisfy numerous (often contradictory) requirements. Examples of such criteria are a guarantee of finding optimal solutions, high-speed operation, the possibility of operation in partially observable environments, etc. This paper provides a survey of modern methods for multi-agent path finding. Special attention is given to various settings of the problem. The differences and between learnable and nonlearnable solution methods and their applicability are discussed. Experimental programming environments necessary for implementing learnable approaches are analyzed separately.