Collective Robots Navigation by Reinforcement Learning Mechanisms with Common Knowledge Field ––an Approach for Heterogeneous-agents Systems––

In this study, we propose a new approach to realize a reinforcement learning scheme for heterogeneous multiagent systems. In our approach, we treat the collective agents systems in which there are multiple autonomous mobile robots, and given tasks are achieved based on the collective behavior approach. Also, each agent organizes and refines its knowledge for executing its own behaviors by reinforcement learning mechanisms. So, we in this study attempt to discuss the reinforcement learning mechanism by which the common knowledge is effectively learned in heterogeneous-agents systems. In our approach, a common knowledge field is generated, and then leaned rule formed knowledge is embedded in that field. Proposed reinforcement learning mechanism is constructed based on learning classifier systems. In heterogeneous agent systems, however, usefulness and representation of certain knowledge is very different for each agent’s role or characteristic. Therefore each agent adaptively learns the way of interpretation and exploitation of common knowledge. For this purpose, an extended model of learning classifier systems is defined to apply the model to heterogeneous-agents systems containing the common knowledge field. We perform computer simulations for multiagent escaping problems to examine our proposed method. The results of experiments illustrate advantages of the proposed mechanism.