Toward a Solution to Multi-agent Credit Assignment Problem

Multi-agent systems (MAS) try to formulate dynamic world which surround human being in every aspect of his life. One of the important challenges encountered in multi-agent systems is the credit assignment problem, simply means distributing the result of the work of a group of agents, such that every agent will have the capability of individual learning. This paper presents the result of a solution suggested for multi-agent credit assignment problem. With the help of observing history of credit assignment in the environment, we will understand what actions are reward-deserving. Results are reported on a multi-agent domain, addition agents.