output
20222026
most citedFinding mixed-strategy equilibria of continuous-action games without gradients using randomized policy networks

3 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

6 papers

cs.GT2026

Efficient representations for team and imperfect-recall equilibrium computation

Luca Carminati, Brian Hu Zhang, Federico Cacciamani +4

Equilibrium finding in two-player zero-sum games with perfect recall is a well-studied topic that has led to many breakthroughs in computational game theory. This paper aims to gen…

cs.GT2024

Joint-perturbation simultaneous pseudo-gradient

Carlos Martin, Tuomas Sandholm

We study the problem of computing an approximate Nash equilibrium of a game whose strategy space is continuous without access to gradients of the utility function. Such games arise…

cs.GT2024

Layered Graph Security Games

Jakub Černý, Chun Kai Ling, Christian Kroer +1

Security games model strategic interactions in adversarial real-world applications. Such applications often involve extremely large but highly structured strategy sets (e.g., selec…

cs.LG2024★ 2 cited

Temporal Graph ODEs for Irregularly-Sampled Time Series

Alessio Gravina, Daniele Zambon, Davide Bacciu +1

Modern graph representation learning works mostly under the assumption of dealing with regularly sampled temporal graph snapshots, which is far from realistic, e.g., social network…

cs.AI2024★ 2 cited

Scalable Mechanism Design for Multi-Agent Path Finding

Paul Friedrich, Yulun Zhang, Michael Curry +5

Multi-Agent Path Finding (MAPF) involves determining paths for multiple agents to travel simultaneously and collision-free through a shared area toward given goal locations. This p…

cs.GT2022★ 3 cited

Finding mixed-strategy equilibria of continuous-action games without gradients using randomized policy networks

Carlos Martin, Tuomas Sandholm

We study the problem of computing an approximate Nash equilibrium of continuous-action game without access to gradients. Such game access is common in reinforcement learning settin…