Abstract
In this paper, a novel online reinforcement learning (RL) algorithm is presented to solve the optimal tracking control (OTC) problem of non-zero-sum (NZS) games for multi-player systems. We first build the augmented system, and then derive the N augmented coupled game algebraic Riccati equations (CGARE). In addition, a online policy iteration (PI) approach is proposed to solve CGARE without requiring any knowledge of the system dynamics. Finally, the effectiveness of our proposed approach is illustrated by numerical simulation.
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