Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments

• URL: http://arxiv.org/abs/2209.10958v1
• Date: Thu, 22 Sep 2022 12:28:29 GMT
• Title: Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
• Authors: Ian Gemp, Thomas Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome Connor, Vibhavari Dasagi, Bart De Vylder, Edgar Duenez-Guzman, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, Siqi Liu, Luke Marris, Kevin R. McKee, Paul Muller, Julien Perolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls
• Abstract summary: The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning. A signature aim of our group is to use the resources and expertise made available to us at DeepMind in deep reinforcement learning.
• Score: 38.16072318606355
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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