An overview of optimal consensus for data driven multi-agent system based on reinforcement learning - Jinna LI; Weiran CHENG (智能科学与技术学报)
Multi-agent system has attracted extensive attention in the past two decades because of its potential applications in engineering, social science and natural science, etc.To achieving the consensus of multi-agent system, it is usually necessary to solve the correlation matrix equation to design the control protocol offline, which requires system model to be known accurately.However, the actual multi-agent system has the characteristics of large-scale, nonlinear coupling, and dynamic change of environment, which makes it very difficult to accurately model the system.This brings challenges to the design of model dependent multi-agent consensus protocol.Reinforcement learning is widely used to solve the optimal control and decision-making problems of complex systems because it can learn the optimal solution of control problems in real time by using the measurement data along the trajectory of the system.The existing theories and methods of online solving the optimal consensus of multi-agent system inreal-time by using reinforcement learning technology were summarized.The application of data-driven reinforcement learning technology in multi-agent system optimal consensus was introduced from the aspects of continuous and discrete, homogenous and heterogeneous, anti-interference robustness and so on.Finally, the future research direction of the optimal consensus problem of multi-agent system based on data-driven technology was discussed.