Self-Optimization in Smart Production Systems using Distributed Reinforcement Learning
Dorothea Schwung, Madhav Modali, Andreas Schwung · 2019
This paper introduces a novel approach for self-learning in highly flexible, modular manufacturing systems enabling fast reconfiguration and online adaptation to changing production requirements. The approach is based on a distributed optimization scheme such that production modules are equipped with their own optimization agent with its local objectives to be optimized. The communication and coordination of the agent is limited to the basically required amount. The approach is based on the recently developed deep deterministic policy gradient (DDPG) approach, a high performing algorithm from the family of actor-critic reinforcement learning algorithms. As DDPG is based on single agent learning, we develop a fully distributed multi-agent learning setting with different levels of information about the neighbors. We apply the approach to a laboratory scale distributed bulk good production testbed with very encouraging results. Particularly, we found very reasonable control strategies by learning the agents from scratch.