Comparison of Different ACS Methods and Analysis about Efficiency of Novel ACS Approaches

Abstract

The purpose of this paper is to present a novel multi-agent cooperating learning method for the learning agents to share episodes beneficial to the exploitation of the accumulated knowledge and to utilize the learned reinforcement values efficiently. Further, taking the visited times into account, this paper proposes the multi-agent learning method that the learning agents share better policies beneficial to the exploration during agent's learning processes. Meanwhile, in the light of the indirect media communication among heterogeneous multi-agents, this paper presents a heterogeneous multi-agent RL method. The agents in our methods are given a simply cooperating way exchanging information in the form of reinforcement values updated in the common model of all agents. Owning the advantages of exploring the unknown environment actively and exploiting learned knowledge effectively, the proposed methods are able to solve both MDPs and combinatorial optimization problems effectively. This paper makes detail comparison of different ACS Methods and analyzes the efficiency of the novel ACS approaches. To results of simulations on the hunter game and the travelling salesman problem, this paper discusses the role of the indirect media communication on the multi-agent cooperation learning system and analyzes its efficiency. The results of experiments also demonstrate that our methods perform competitively with representative methods on each domain respectively