  Optimal tracking of continuous time nonlinear systems has been extensively
studied in literature. However, in several applications, absence of knowledge
about system dynamics poses a severe challenge to solving the optimal tracking
problem. This has found growing attention among researchers recently, and
integral reinforcement learning (IRL)-based method augmented with actor neural
network (NN) have been deployed to this end. However, very few studies have
been directed to model-free $H_{\infty}$ optimal tracking control that helps in
attenuating the effect of disturbances on the system performance without any
prior knowledge about system dynamics. To this end a recursive least
square-based parameter update was recently proposed. However, gradient
descent-based parameter update scheme is more sensitive to real-time variation
in plant dynamics. And experience replay (ER) technique has been shown to
improve the convergence of NN weights by utilizing past observations
iteratively. Motivated by these, this paper presents a novel parameter update
law based on variable gain gradient descent and experience replay technique for
tuning the weights of critic, actor and disturbance NNs.
