Stackelberg games for model-free continuous-time stochastic systems based on adaptive dynamic programming

摘要

Solving the Stackelberg game problem generally needs full data of the system. In this paper, two online adaptive dynamic programming algorithms are proposed to solve the Stackelberg game problem for model-free linear continuous-time systems subject to multiplicative noise. Stackelberg games are based on two different strategies: Nash-based Stackelberg strategy and Pareto-based Stackelberg strategy. We apply directly the state and input information to iteratively update Stackelberg games online. The effectiveness of the algorithms is verified by two simulation examples.