2024/01/23 by Denizalp Goktas, Arjun Prakash, Goktas, Denizalp +3 · 1 citation
Mathematics · #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #Markov Chains and Monte Carlo Methods
paper · pdf · doi:10.48550/arxiv.2401.12437
openalex publication_date 2024/01/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Zero-sum Markov Stackelberg games can be used to model myriad problems, in domains ranging from economics to human robot interaction. In this paper, we develop policy gradient methods that solve these games in continuous state and action settings using noisy gradient estimates computed from observed trajectories of play. When the games are convex-concave, we prove that our algorithms converge to Stackelberg equilibrium in polynomial time. We also show that reach-avoid problems are naturally modeled as convex-concave zero-sum Markov Stackelberg games, and that Stackelberg equilibrium policies are more effective than their Nash counterparts in these problems.