MATHEMATICAL MODEL BASED ON MULTI-AGENT REINFORCEMENT LEARNING (MARL) AND PARTIALLY OBSERVABLE MARKOV DECISION PROCESS (POMDP) FOR MODELING CARGO MOVEMENT FOR A MOBILE ROBOTS GROUP

This paper presents a mathematical model that combines multi-agent reinforcement learning (MARL) and partially observable Markov decision process (POMDP) to model the task of moving cargo by a mobile robots group. The use of such methods allows agents to effectively interact under conditions of incomplete information, optimizing task performance in real, dynamic environments. This model has significant potential for process automation in industrial and logistics systems.