Concurrent learning-based online approximate feedback-Nash equilibrium solution of N-player nonzero-sum differential games

Abstract

This paper presents a concurrent learning-based actor-critic-identifier architecture to obtain an approximate feedback-Nash equilibrium solution to an infinite horizon N-player nonzero-sum differential game online, without requiring persistence of excitation (PE), for a nonlinear control-affine system. Under a condition milder than PE, uniformly ultimately bounded convergence of the developed control policies to the feedback-Nash equilibrium policies is established.