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20222025
most citedadaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential-Decision Making Human-in-the-Loop Systems

13 citations · 15 across the 5 of their papers we have counts for

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2 papers · 1 filter

cs.GT2024

Computing Nash Equilibria in Potential Games with Private Uncoupled Constraints

Nikolas Patris, Stelios Stavroulakis, Fivos Kalogiannis +2

We consider the problem of computing Nash equilibria in potential games where each player's strategy set is subject to private uncoupled constraints. This scenario is frequently en…

cs.GT2022★ 2 cited

Efficiently Computing Nash Equilibria in Adversarial Team Markov Games

Fivos Kalogiannis, Ioannis Anagnostides, Ioannis Panageas +3

Computing Nash equilibrium policies is a central problem in multi-agent reinforcement learning that has received extensive attention both in theory and in practice. However, provab…