| Abstract: | This work presents a novel framework based on adaptive learning techniques to solve the continuous‐time open‐loop Stackelberg games. The method yields real‐time approximations of the game value and convergence of the policies to the open‐loop Stackelberg‐equilibrium solution, while also guaranteeing asymptotic stability of the equilibrium point of the closed‐loop system. It is implemented as a separate actor/critic parametric network approximator structure for every player and involves simultaneous continuous‐time adaptation. To introduce and implement the hierarchical structure to the coupled optimization problem, we adjoin to the leader the controller dynamics of the follower. A persistence of excitation condition guarantees convergence of both critics to the actual game values that eventually solve the hierarchical optimization problem. A simulation example shows the efficacy of the proposed approach. |
