An Algorithm of the Collective Interaction of Intelligent Agents in Centralized Multi-Agent Systems

Abstract

A centralized multiagent system based on the methods of feudal reinforcement learning, including agents-managers and agents-subordinates, is considered. A brief review of the author’s previous works on this topic is given. The standard algorithm for the functioning of systems of this type is considered, including the translation of the decision maker to agents-managers, the division of tasks by agents-managers into a set of subtasks, the choice by the agent-manager of the strategy used, the formation of reward functions by agents-managers, the assignment of tasks to agents-subordinates, the execution by agents-subordinates assigned tasks. The main problems of this algorithm are presented, changes are made to ensure the possibility of automatically assigning agent-managers and forming groups of subordinate agents around them, reproducing and exchanging experience. More attention is paid to the problem of experience exchange, the main ways of experience exchange are given. The principles of operation of a machine vision system that implements an upgraded algorithm are described. An assessment of the effectiveness of the obtained algorithm for the collective interaction of intelligent agents using a software model developed in Microsoft Unity is given. A comparison is made between the standard algorithm for multiagent interaction and the proposed algorithm for the collective interaction of intelligent agents in centralized multi-agent systems based on the approach of reinforcement learning and visualization of three-dimensional scenes. The conclusion is made about the expediency of using the developed algorithmt.