Convex-Concave Zero-sum Markov Stackelberg Games

Fuente: arXiv
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Autores principales: Goktas, Denizalp, Prakash, Arjun, Greenwald, Amy
Formato: Preprint
Publicado: 2024
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author Goktas, Denizalp
Prakash, Arjun
Greenwald, Amy
author_facet Goktas, Denizalp
Prakash, Arjun
Greenwald, Amy
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2401_12437
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Convex-Concave Zero-sum Markov Stackelberg Games
Goktas, Denizalp
Prakash, Arjun
Greenwald, Amy
Computer Science and Game Theory
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.
title Convex-Concave Zero-sum Markov Stackelberg Games
topic Computer Science and Game Theory
url https://arxiv.org/abs/2401.12437