Poster
Convex-Concave Zero-Sum Markov Stackelberg Games
Denizalp Goktas · Arjun Prakash · Amy Greenwald
Great Hall & Hall B1+B2 (level 1) #2013
Abstract:
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.
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