  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.
