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Volodymyr Mnih
Researcher at Google
Publications - 62
Citations - 51796
Volodymyr Mnih is an academic researcher from Google. The author has contributed to research in topics: Reinforcement learning & Artificial neural network. The author has an hindex of 37, co-authored 60 publications receiving 38272 citations. Previous affiliations of Volodymyr Mnih include University of Toronto & University of Alberta.
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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.
Posted Content
Playing Atari with Deep Reinforcement Learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Alex Graves,Ioannis Antonoglou,Daan Wierstra,Martin Riedmiller +6 more
TL;DR: This work presents the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning, which outperforms all previous approaches on six of the games and surpasses a human expert on three of them.
Proceedings Article
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih,Adrià Puigdomènech Badia,Mehdi Mirza,Alex Graves,Tim Harley,Timothy P. Lillicrap,David Silver,Koray Kavukcuoglu +7 more
TL;DR: A conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers and shows that asynchronous actor-critic succeeds on a wide variety of continuous motor control problems as well as on a new task of navigating random 3D mazes using a visual input.
Posted Content
Recurrent Models of Visual Attention
TL;DR: In this article, a recurrent neural network (RNN) model is proposed to extract information from an image or video by adaptively selecting a sequence of regions or locations and only processing the selected regions at high resolution.
Proceedings Article
Recurrent Models of Visual Attention
TL;DR: A novel recurrent neural network model that is capable of extracting information from an image or video by adaptively selecting a sequence of regions or locations and only processing the selected regions at high resolution is presented.