An overview of optimal consensus for data driven multi-agent system based on reinforcement learning

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