activity
20132026
most citedHuman-level performance in first-person multiplayer games with population-based deep reinforcement learning

693 citations · 3.6k across the 81 of their papers we have counts for

collaborators
Showing 2016Show all

7 papers · 1 filter

cs.AI2016★ 39 cited

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…

cs.LG2016★ 271 cited

Reinforcement Learning with Unsupervised Auxiliary Tasks

Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki +4

Deep reinforcement learning agents have achieved state-of-the-art results by directly maximising cumulative reward. However, environments contain a much wider variety of possible t…

cs.LG2016★ 380 cited

Learning to reinforcement learn

Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala +6

In recent years deep reinforcement learning (RL) systems have attained superhuman performance in a number of challenging task domains. However, a major limitation of such applicati…

stat.ML2016★ 154 cited

Using Fast Weights to Attend to the Recent Past

Jimmy Ba, Geoffrey Hinton, Volodymyr Mnih +2

Until recently, research on artificial neural networks was largely restricted to systems with only two types of variable: Neural activities that represent the current or recent inp…

stat.ML2016★ 168 cited

Model-Free Episodic Control

Charles Blundell, Benigno Uria, Alexander Pritzel +6

State of the art deep reinforcement learning algorithms take many millions of interactions to attain human-level performance. Humans, on the other hand, can very quickly exploit hi…

cs.NE2016★ 8 cited

View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation

Joel Z. Leibo, Qianli Liao, Winrich Freiwald +2

The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and rela…

cs.AI2016

Building Machines That Learn and Think Like People

Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum +1

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks t…