activity
20132026
most citedHuman-level performance in first-person multiplayer games with population-based deep reinforcement learning

693 citations · 3.6k across the 81 of their papers we have counts for

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6 papers · 1 filter

cs.GT2025

Quantifying the Self-Interest Level of Markov Social Dilemmas

Richard Willis, Yali Du, Joel Z Leibo +1

This paper introduces a novel method for estimating the self-interest level of Markov social dilemmas. We extend the concept of self-interest level from normal-form games to Markov…

cs.GT2024

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.…

cs.GT2023

Resolving social dilemmas with minimal reward transfer

Richard Willis, Yali Du, Joel Z Leibo +1

Social dilemmas present a significant challenge in multi-agent cooperation because individuals are incentivised to behave in ways that undermine socially optimal outcomes. Conseque…

cs.GT2020

Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games

Edward Hughes, Thomas W. Anthony, Tom Eccles +3

Zero-sum games have long guided artificial intelligence research, since they possess both a rich strategy space of best-responses and a clear evaluation metric. What's more, compet…

cs.GT2018

A Generalised Method for Empirical Game Theoretic Analysis

Karl Tuyls, Julien Perolat, Marc Lanctot +2

This paper provides theoretical bounds for empirical game theoretical analysis of complex multi-agent interactions. We provide insights in the empirical meta game showing that a Na…

cs.GT2017

Symmetric Decomposition of Asymmetric Games

Karl Tuyls, Julien Perolat, Marc Lanctot +6

We introduce new theoretical insights into two-population asymmetric games allowing for an elegant symmetric decomposition into two single population symmetric games. Specifically,…