Planning and Learning in Multi-Agent Path Finding

Abstract 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 other for this problem, there are no universal solution methods that simultaneously satisfy (often contradictory) requirements. Examples such criteria guarantee optimal solutions, high-speed operation, possibility operation partially observable environments, etc. This paper provides survey modern multi-agent finding. Special attention given to various settings problem. The differences and between learnable nonlearnable their applicability discussed. Experimental programming environments necessary implementing approaches analyzed separately.