The Impact of Communication and Memory in State-Based Potential Game-based Distributed Optimization

In this paper, we discuss the impact of communication and memory-based learners on distributed self-optimization of smart and flexible manufacturing units. Specifically, we employ the recently proposed framework of state-based potential games, which has proven to be successful in allowing distributed optimization in multi-agent systems. We first augment the framework with additional communication capabilities for the individual players and analyze the efficacy of state and action communications within the different players. Second, we incorporate memory states within the learning dynamics of the players and analyze their impact on the learning performance. The proposed method is inspired by the promising results of memory-based reinforcement learning. However, previous studies have rarely dealt with distributed manufacturing control. We believe that it will be important to explore the potential use of the communication and memory-based approaches in manufacturing control with multi-agent settings. Hence, the proposed method is applied to a bulk good laboratory plant providing a thorough experimental analysis of the effect of the various improvements with very encouraging results.