activity
20182026
most citedHuman-level performance in first-person multiplayer games with population-based deep reinforcement learning

693 citations · 739 across the 24 of their papers we have counts for

collaborators
Showing 2023Show all

5 papers · 1 filter

cs.AI2023★ 1 cited

Evaluating Agents using Social Choice Theory

Marc Lanctot, Kate Larson, Yoram Bachrach +6

We argue that many general evaluation problems can be viewed through the lens of voting theory. Each task is interpreted as a separate voter, which requires only ordinal rankings o…

cs.GT2023★ 2 cited

Approximating Nash Equilibria in Normal-Form Games via Stochastic Optimization

Ian Gemp, Luke Marris, Georgios Piliouras

We propose the first loss function for approximate Nash equilibria of normal-form games that is amenable to unbiased Monte Carlo estimation. This construction allows us to deploy s…

cs.GT2023

Equilibrium-Invariant Embedding, Metric Space, and Fundamental Set of Normal-Form Games

Luke Marris, Ian Gemp, Georgios Piliouras

Equilibrium solution concepts of normal-form games, such as Nash equilibria, correlated equilibria, and coarse correlated equilibria, describe the joint strategy profiles from whic…

cs.GT2023

Generative Adversarial Equilibrium Solvers

Denizalp Goktas, David C. Parkes, Ian Gemp +5

We introduce the use of generative adversarial learning to compute equilibria in general game-theoretic settings, specifically the generalized Nash equilibrium (GNE) in pseudo-game…

cs.AI2023★ 1 cited

Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning

Zun Li, Marc Lanctot, Kevin R. McKee +7

Opponent modeling methods typically involve two crucial steps: building a belief distribution over opponents' strategies, and exploiting this opponent model by playing a best respo…