# Convex-Concave Zero-sum Markov Stackelberg Games

arXiv: https://arxiv.org/abs/2401.12437 · HTML: https://mlsanity.com/paper/2401.12437/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2024.

## Built on by (most cited first, 1 of 1)

- [Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2410.16600/) (2024)

## Abstract (arXiv)

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.