Event‐triggered Neural Experience Replay Learning for Nonzero‐sum Tracking Games of Unknown Continuous‐time Nonlinear Systems

In this paper, an online event‐triggered integral reinforcement learning is proposed to solve the nonzero‐sum tracking games of the two‐player nonlinear system with unknown dynamics and constrained input. Firstly, an augmented system of the nonzero‐sum game is constructed to describe the tracking problem. Thus, the optimal tracking problem is treated as solving the coupled Hamilton‐Jacobi (HJ) equations of the augmented system. To restrict the number of sampling states, a novel triggering threshold containing the augmented states is designed, and it avoids the existence of Zeno behavior. Secondly, a single‐critic network is utilized to approximate the solution of the coupled HJ equations. The critic network turning law with experience replay technology relaxes the dependence on the persistence of excitation (PE) conditions in traditional integral reinforcement learning (IRL) by using historical data in the stack. Moreover, the augmented system states and the critic NN weight errors are uniformly ultimate boundedness (UUB) by Lyapunov theory. Simulation examples are provided to demonstrate the availability of the proposed algorithm.