693 citations · 1.3k across the 11 of their papers we have counts for
11 papers
Modeling human reputation-seeking behavior in a spatio-temporally complex public good provision game
Edward Hughes, Tina O. Zhu, Martin J. Chadwick +6
Multi-agent reinforcement learning algorithms are useful for simulating social behavior in settings that are too complex for other theoretical approaches like game theory. However,…
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot
Joel Z. Leibo, Edgar Duéñez-Guzmán, Alexander Sasha Vezhnevets +7
Existing evaluation suites for multi-agent reinforcement learning (MARL) do not assess generalization to novel situations as their primary objective (unlike supervised-learning ben…
Inferring a Continuous Distribution of Atom Coordinates from Cryo-EM Images using VAEs
Dan Rosenbaum, Marta Garnelo, Michal Zielinski +10
Cryo-electron microscopy (cryo-EM) has revolutionized experimental protein structure determination. Despite advances in high resolution reconstruction, a majority of cryo-EM experi…
A multi-agent reinforcement learning model of reputation and cooperation in human groups
Kevin R. McKee, Edward Hughes, Tina O. Zhu +7
Collective action demands that individuals efficiently coordinate how much, where, and when to cooperate. Laboratory experiments have extensively explored the first part of this pr…
Quantifying the effects of environment and population diversity in multi-agent reinforcement learning
Kevin R. McKee, Joel Z. Leibo, Charlie Beattie +1
Generalization is a major challenge for multi-agent reinforcement learning. How well does an agent perform when placed in novel environments and in interactions with new co-players…
DeepMind Lab2D
Charles Beattie, Thomas Köppe, Edgar A. Duéñez-Guzmán +1
We present DeepMind Lab2D, a scalable environment simulator for artificial intelligence research that facilitates researcher-led experimentation with environment design. DeepMind L…
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning +15
Recent progress in artificial intelligence through reinforcement learning (RL) has shown great success on increasingly complex single-agent environments and two-player turn-based g…
Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents
Joel Z. Leibo, Cyprien de Masson d'Autume, Daniel Zoran +10
Psychlab is a simulated psychology laboratory inside the first-person 3D game world of DeepMind Lab (Beattie et al. 2016). Psychlab enables implementations of classical laboratory…
A multi-agent reinforcement learning model of common-pool resource appropriation
Julien Perolat, Joel Z. Leibo, Vinicius Zambaldi +3
Humanity faces numerous problems of common-pool resource appropriation. This class of multi-agent social dilemma includes the problems of ensuring sustainable use of fresh water, c…
DeepMind Lab
Charles Beattie, Joel Z. Leibo, Denis Teplyashin +18
DeepMind Lab is a first-person 3D game platform designed for research and development of general artificial intelligence and machine learning systems. DeepMind Lab can be used to s…
Massively Parallel Methods for Deep Reinforcement Learning
Arun Nair, Praveen Srinivasan, Sam Blackwell +11
We present the first massively distributed architecture for deep reinforcement learning. This architecture uses four main components: parallel actors that generate new behaviour; p…