Andreas Alexander Haupt — accepted AI-conference papers

2025

Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning

Ian Gemp, Andreas Alexander Haupt, Luke Marris, Siqi Liu, Georgios Piliouras

ICML 2025poster

Behavioral diversity, expert imitation, fairness, safety goals and others give rise to preferences in sequential decision making domains that do not decompose additively across time. We introduce the class of convex Markov games that allow general convex preferences over occupancy measures. Despite…

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2023

Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games

Brian Hu Zhang, Gabriele Farina, Ioannis Anagnostides, Federico Cacciamani, Stephen Marcus McAleer, Andreas Alexander Haupt +4

NeurIPS 2023poster

We introduce a new approach for computing optimal equilibria via learning in games. It applies to extensive-form settings with any number of players, including mechanism design, information design, and solution concepts such as correlated, communication, and certification equilibria. We observe that…

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