AUTHOR=Xu Lei-Xin , Chen Yang-Yang TITLE=Deep Reinforcement Learning Algorithms for Multiple Arc-Welding Robots JOURNAL=Frontiers in Control Engineering VOLUME=2 YEAR=2021 URL=https://www.frontiersin.org/journals/control-engineering/articles/10.3389/fcteg.2021.632417 DOI=10.3389/fcteg.2021.632417 ISSN=2673-6268 ABSTRACT=

The applications of the deep reinforcement learning method to achieve the arcs welding by multi-robot systems are presented, where the states and the actions of each robot are continuous and obstacles are considered in the welding environment. In order to adapt to the time-varying welding task and local information available to each robot in the welding environment, the so-called multi-agent deep deterministic policy gradient (MADDPG) algorithm is designed with a new set of rewards. Based on the idea of the distributed execution and centralized training, the proposed MADDPG algorithm is distributed. Simulation results demonstrate the effectiveness of the proposed method.