Towards Pick and Place Multi Robot Coordination Using Multi-agent Deep Reinforcement Learning

Recent advances in deep reinforcement learning are enabling the creation and use of powerful multi-agent systems in complex areas such as multi-robot coordination. These show great promise to help solve many of the difficult challenges of rapidly growing domains such as smart manufacturing. In this position paper we describe our early-stage work on the use of multi-agent deep reinforcement learning to optimise coordination in a multi-robot pick and place system. Our goal is to evaluate the feasibility of this new approach in a manufacturing environment. We propose to adopt a decentralised partially observable Markov Decision Process approach and to extend an existing cooperative game work to suitably formulate the problem as a multi-agent system. We describe the centralised training/decentralised execution multi-agent learning approach which allows a group of agents to be trained simultaneously but to exercise decentralised control based on their local observations. We identify potential learning algorithms and architectures that we will investigate as a base for our implementation and we outline our open research questions. Finally we identify next steps in our research program.