Addition of learning to critic agent as a solution to the multi-agent credit assignment problem

Multi-agent systems (MAS) is a solution to the nowadays encountered problems, which have the characteristics such as distributiveness, dynamism and the need to adaptation, robustness, efficiency, and reusability. This paper proposed a solution to multi-agent credit assignment problem. The contribution is to equip the critic agent (who is responsible for distributing reinforcements among agents) with learning capability. Some criteria are used to propose an inner feedback to the critic. Results of simulation show the applicability of the method to a task, which has the characteristic that the agent has to decide from a large set of actions. The research is a preliminary step to more in-depth thinking for a solution to multi-agent critic assignment.