An improved learning approach in Multi-agent system

This paper deals with the topic of learning in the reactive multi-agent system. The key question addressed is how several agents learn to coordinate their actions so that they could resolve a given environmental task together. In approaching this question, two constraints will have to be taken into consideration: one is the incompatibility constraint. that is. the fact that different actions may be mutually exclusives and the other is the local information constraint. that is, the fact that typically each agent knows only a fraction of the environment. The agent is selfish on its own. In order to gain maximal group benefit from the multi-agent system (MAS) learning. this paper attempts to present an improved approach of learning in MAS. This approach, which is based on the organization-structure and dynamic-weight by considering the credit of the agent, is an improvement of the learning method for better outcomes.