The article presents a hybrid algorithm for the formation of the shortest trajectory for intelligent agents of a multi-agent system, based on the synthesis of methods of the reinforcement learning paradigm, the heuristic search algorithm A*, which has the functions of exchange of experience, as well as the automatic formation of subgroups of agents based on their visibility areas. The experimental evaluation of the developed algorithm was carried out by simulating the task of finding the target state in the maze in the Microsoft Unity environment. The results of the experiment showed that the use of the developed hybrid algorithm made it possible to reduce the time for solving the problem by an average of 12.7 % in comparison with analogs. The differences between the proposed new “hybrid algorithm for the formation of the shortest trajectory based on the use of multi-agent reinforcement learning, search algorithm A* and exchange of experience” from analogs are as follows: – application of the algorithm for the formation of subgroups of subordinate agents based on the “scope” of the leader agent for the implementation of a multi-level hierarchical system for managing a group of agents; – combining the principles of reinforcement learning and the search algorithm A*.