Event-triggered integral reinforcement learning for nonzero-sum games with asymmetric input saturation

Abstract

In this paper, an event-triggered integral reinforcement learning (IRL) algorithm is developed for the nonzero-sum game problem with asymmetric input saturation. First, for each player, a novel non-quadratic value function with a discount factor is designed, and the coupled Hamilton–Jacobi equation that does not require a complete knowledge of the game is derived by using the idea of IRL. Second, the execution of each player is based on the event-triggered mechanism. In the implementation, an adaptive dynamic programming based learning scheme using a single critic neural network (NN) is developed. Experience replay technique is introduced into the classical gradient descent method to tune the weights of the critic NN. The stability of the system and the elimination of Zeno behavior are proved. Finally, simulation experiments verify the effectiveness of the event-triggered IRL algorithm.