K
Karl Tuyls
Researcher at University of Liverpool
Publications - 326
Citations - 8965
Karl Tuyls is an academic researcher from University of Liverpool. The author has contributed to research in topics: Reinforcement learning & Game theory. The author has an hindex of 45, co-authored 313 publications receiving 7071 citations. Previous affiliations of Karl Tuyls include Google & University of Hasselt.
Papers
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Proceedings Article
Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward
Peter Sunehag,Guy Lever,Audrunas Gruslys,Wojciech Marian Czarnecki,Vinicius Zambaldi,Max Jaderberg,Marc Lanctot,Nicolas Sonnerat,Joel Z. Leibo,Karl Tuyls,Thore Graepel +10 more
TL;DR: This work addresses the problem of cooperative multi-agent reinforcement learning with a single joint reward signal by training individual agents with a novel value decomposition network architecture, which learns to decompose the team value function into agent-wise value functions.
Proceedings Article
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
Marc Lanctot,Vinicius Zambaldi,Audrunas Gruslys,Angeliki Lazaridou,Karl Tuyls,Julien Perolat,David Silver,Thore Graepel +7 more
TL;DR: In this article, a meta-algorithm for general MARL is proposed, based on approximate best responses to mixtures of policies generated using deep reinforcement learning, and empirical game theoretic analysis to compute meta-strategies for policy selection.
Posted Content
Value-Decomposition Networks For Cooperative Multi-Agent Learning
Peter Sunehag,Guy Lever,Audrunas Gruslys,Wojciech Marian Czarnecki,Vinicius Zambaldi,Max Jaderberg,Marc Lanctot,Nicolas Sonnerat,Joel Z. Leibo,Karl Tuyls,Thore Graepel +10 more
TL;DR: In this paper, a value decomposition network is proposed to decompose the team value function into agent-wise value functions, which leads to superior results when combined with weight sharing, role information and information channels.
Journal ArticleDOI
Evolutionary dynamics of multi-agent learning: a survey
TL;DR: This article surveys the dynamical models that have been derived for various multi-agent reinforcement learning algorithms, making it possible to study and compare them qualitatively, and provides a roadmap on the progress that has been achieved in analysing the evolutionary dynamics of multi- agent learning.