Paper Info
Title
Developing, evaluating and scaling learning agents in multi-agent environments
Abstract
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks. A signature aim of our group is to use the resources and expertise made available to us at DeepMind in deep reinforcement learning to explore multi-agent systems in complex environments and use these benchmarks to advance our understanding. Here, we summarise the recent work of our team and present a taxonomy that we feel highlights many important open challenges in multi-agent research.
Year
DOI
Venue
2022
10.3233/AIC-220113
AI COMMUNICATIONS
Keywords
DocType
Volume
Game theory, multi-agent, reinforcement learning, equilibrium, mechanism design
Journal
35
Issue
ISSN
Citations 
4
0921-7126
0
PageRank 
References 
Authors
0.34
0
27
Name
Order
Citations
PageRank
Ian M. Gemp1166.37
Anthony, Thomas2233.03
Yoram Bachrach3126279.07
Avishkar Bhoopchand431.04
Kalesha Bullard500.34
Jerome Connor600.34
Vibhavari Dasagi700.34
Bart De Vylder800.34
Edgar A. Duéñez-Guzmán900.68
Romuald Elie105210.37
Richard Everett1100.34
Daniel Hennes1213518.46
Edward Hughes13267.67
Mina Khan1400.34
Marc Lanctot15212197.97
Kate Larson1600.34
Guy Lever1700.34
Siqi Liu18554.94
Luke Marris1900.34
Kevin R. McKee20133.59
Paul Muller2100.34
Julien Perolat227512.64
Florian Strub2317511.20
Andrea Tacchetti241389.57
Eugene Tarassov2500.34
Zhe Wang2600.34
Karl Tuyls271272127.83