Initial Excitation-Based Inverse Reinforcement Learning for Continuous-Time Linear Non-Zero-Sum Games

In this article, the initial excitation-based inverse reinforcement learning methods are presented for continuous-time linear non-zero-sum games. The policy iteration and value iteration algorithms are presented for the inverse reinforcement learning problems, and an online-verifiable initial excitation condition is given to guarantee the convergence of the presented algorithms. Comparing with the traditional inverse reinforcement learning algorithms for linear non-zero-sum games, the presented algorithms relax the requirement on data-storage mechanism. Furthermore, the requirement on the initial stabilizing state feedback matrices is relaxed in the presented initial excitation-based value iteration algorithm. The properties of the presented initial excitation-based policy iteration and value iteration algorithms are analyzed. Simulation results show the efficiency of the presented algorithms.