Data-Driven Reinforcement Learning Design for Multi-agent Systems with Unknown Disturbances
Xiangnan Zhong, Zhen Ni · 2018
In this paper, we develop a new data-driven reinforcement learning method to solve the multi-agent consensus control problem with unknown disturbances. Due to the existence of disturbances, the transmitted information between each pair of the agents becomes unreliable, which makes that the data- driven reinforcement learning based control becomes difficult. Therefore, we solve the problem by developing an appropriate performance index for each agent to convert the robust consensus problem to an auxiliary optimal control problem. The equivalence of the transformation is proved to show that the solution of the auxiliary optimal control system can asymptotically stabilize the original robust system and synchronize all the agents at the same time. Neural network techniques are applied to implement the proposed method. Finally, the simulation results demonstrate the theoretical analysis and verify the effectiveness of the proposed method.