activity
20202026
most citedGame-Theoretic Multiagent Reinforcement Learning

145 citations · 145 across the 15 of their papers we have counts for

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Showing 2025Show all

5 papers · 1 filter

cs.GT2025

General search techniques without common knowledge for imperfect-information games, and application to superhuman Fog of War chess

Brian Hu Zhang, Tuomas Sandholm

Since the advent of AI, games have served as progress benchmarks. Meanwhile, imperfect-information variants of chess have existed for over a century, present extreme challenges, an…

cs.GT2025

Learning a Game by Paying the Agents

Brian Hu Zhang, Tao Lin, Yiling Chen +1

We study the problem of learning the utility functions of no-regret learning agents in a repeated normal-form game. Differing from most prior literature, we introduce a principal w…

cs.MA2025

A Multiagent Path Search Algorithm for Large-Scale Coalition Structure Generation

Redha Taguelmimt, Samir Aknine, Djamila Boukredera +2

Coalition structure generation (CSG), i.e. the problem of optimally partitioning a set of agents into coalitions to maximize social welfare, is a fundamental computational problem…

cs.GT2025

Solving Infinite-Player Games with Player-to-Strategy Networks

Carlos Martin, Tuomas Sandholm

We present a new approach to solving games with a countably or uncountably infinite number of players. Such games are often used to model multiagent systems with a large number of…

cs.GT2025

On the Power of Perturbation under Sampling in Solving Extensive-Form Games

Wataru Masaka, Mitsuki Sakamoto, Kenshi Abe +3

We investigate how perturbation does and does not improve the Follow-the-Regularized-Leader (FTRL) algorithm in solving imperfect-information extensive-form games under sampling, w…