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Continual Reinforcement Learning Formulation For Zero-Sum Game-Based Constrained Optimal Tracking, Behzad Farzanegan, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This study provides a novel reinforcement learning-based optimal tracking control of partially uncertain nonlinear discrete-time (DT) systems with state constraints using zero-sum game (ZSG) formulation. To address optimal tracking, a novel augmented system consisting of tracking error and its integral value, along with an uncertain desired trajectory, is constructed. A barrier function (BF) with a tradeoff factor is incorporated into the cost function to keep the state trajectories to remain within a compact set and to balance safety with optimality. Next, by using the modified value functional, the ZSG formulation is introduced wherein an actor–critic neural network (NN) framework is …