Certainty and Expertness –Based Credit Assignment for Cooperative Q-learning Agents with an and –Type Task

In mulitagent reinforcement learning, inter agent credit assignment is a fundamental problem, since a single scalar reinforcement signal is the only reliable feedback that teams of learning agents receive.This problem is more critical in groups of independent learners with a joint task. In this research, it is assumed that a critic agent receives the environment feedback and assigns a proper credit to each agent using some measures. Three of such measures for a team of cooperative agents with a parallel and AND-type task are introduced. These measures somehow compare the agentsʹ knowledge. One of these criteria, called Normal Expertness, is a non-relative measure while two other ones (Certainty and Relative Normal Expertness ) are relative measure. It is experimentally shown that relative measures work better as they contain more information for the critic agent