Deep Reinforcement Learning Algorithms for Multiple Arc-Welding Robots - Xu, Lei-Xin; Chen, Yang-Yang (Frontiers in Control Engineering, 2021)
The applications of the deep reinforcement learning method to achieve 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, a so-called multi-agent deep deterministic policy gradient (MADDPG) algorithm is designed with a new setting of rewards. Based on the idea of the distributed execution and the centralized training, the proposed MADDPG algorithm is distributed. Simulation results demonstrate the effectiveness of the proposed method.