693 citations · 739 across the 24 of their papers we have counts for
6 papers · 1 filter
Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning
Ian Gemp, Andreas Haupt, Luke Marris +2
Behavioral diversity, expert imitation, fairness, safety goals and others give rise to preferences in sequential decision making domains that do not decompose additively across tim…
Visualizing 2x2 Normal-Form Games: twoxtwogame LaTeX Package
Luke Marris, Ian Gemp, Siqi Liu +2
Normal-form games with two players, each with two strategies, are the most studied class of games. These so-called 2x2 games are used to model a variety of strategic interactions.…
NfgTransformer: Equivariant Representation Learning for Normal-form Games
Siqi Liu, Luke Marris, Georgios Piliouras +2
Normal-form games (NFGs) are the fundamental model of strategic interaction. We study their representation using neural networks. We describe the inherent equivariance of NFGs -- a…
Approximating the Core via Iterative Coalition Sampling
Ian Gemp, Marc Lanctot, Luke Marris +11
The core is a central solution concept in cooperative game theory, defined as the set of feasible allocations or payments such that no subset of agents has incentive to break away…
Steering Language Models with Game-Theoretic Solvers
Ian Gemp, Roma Patel, Yoram Bachrach +6
Mathematical models of interactions among rational agents have long been studied in game theory. However these interactions are often over a small set of discrete game actions whic…
Neural Population Learning beyond Symmetric Zero-sum Games
Siqi Liu, Luke Marris, Marc Lanctot +3
We study computationally efficient methods for finding equilibria in n-player general-sum games, specifically ones that afford complex visuomotor skills. We show how existing metho…