학술논문
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
Document Type
Working Paper
Author
Gemp, Ian; Anthony, Thomas; Bachrach, Yoram; Bhoopchand, Avishkar; Bullard, Kalesha; Connor, Jerome; Dasagi, Vibhavari; De Vylder, Bart; Duenez-Guzman, Edgar; Elie, Romuald; Everett, Richard; Hennes, Daniel; Hughes, Edward; Khan, Mina; Lanctot, Marc; Larson, Kate; Lever, Guy; Liu, Siqi; Marris, Luke; McKee, Kevin R.; Muller, Paul; Perolat, Julien; Strub, Florian; Tacchetti, Andrea; Tarassov, Eugene; Wang, Zhe; Tuyls, Karl
Source
Subject
Language
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.
Comment: Published in AI Communications 2022
Comment: Published in AI Communications 2022