G
Georg Ostrovski
Researcher at Google
Publications - 36
Citations - 28764
Georg Ostrovski is an academic researcher from Google. The author has contributed to research in topics: Reinforcement learning & Computer science. The author has an hindex of 17, co-authored 31 publications receiving 19938 citations. Previous affiliations of Georg Ostrovski include University of Warwick.
Papers
More filters
Journal ArticleDOI
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
Journal ArticleDOI
Hybrid computing using a neural network with dynamic external memory
Alex Graves,Greg Wayne,Malcolm Reynolds,Tim Harley,Ivo Danihelka,Agnieszka Grabska-Barwinska,Sergio Gomez Colmenarejo,Edward Grefenstette,Tiago Ramalho,John P. Agapiou,Adrià Puigdomènech Badia,Karl Moritz Hermann,Yori Zwols,Georg Ostrovski,Adam Cain,Helen King,Christopher Summerfield,Phil Blunsom,Koray Kavukcuoglu,Demis Hassabis +19 more
TL;DR: A machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer.
Posted Content
Rainbow: Combining Improvements in Deep Reinforcement Learning
Matteo Hessel,Joseph Modayil,Hado van Hasselt,Tom Schaul,Georg Ostrovski,Will Dabney,Dan Horgan,Bilal Piot,Mohammad Gheshlaghi Azar,David Silver +9 more
TL;DR: This paper examines six extensions to the DQN algorithm and empirically studies their combination, showing that the combination provides state-of-the-art performance on the Atari 2600 benchmark, both in terms of data efficiency and final performance.
Proceedings Article
Rainbow: Combining Improvements in Deep Reinforcement Learning
Matteo Hessel,Joseph Modayil,Hado van Hasselt,Tom Schaul,Georg Ostrovski,Will Dabney,Dan Horgan,Bilal Piot,Mohammad Gheshlaghi Azar,David Silver +9 more
TL;DR: In this article, the authors examined six extensions to the DQN algorithm and empirically studied their combination, showing that the combination provided state-of-the-art performance on the Atari 2600 benchmark.
Proceedings Article
Unifying count-based exploration and intrinsic motivation
TL;DR: In this paper, the authors use density models to measure uncertainty and derive a pseudo-count from an arbitrary density model, which can be used to improve exploration in non-tabular reinforcement learning.