Journal ArticleDOI
Mastering the game of Go with deep neural networks and tree search
David Silver,Aja Huang,Chris J. Maddison,Arthur Guez,Laurent Sifre,George van den Driessche,Julian Schrittwieser,Ioannis Antonoglou,Veda Panneershelvam,Marc Lanctot,Sander Dieleman,Dominik Grewe,John Nham,Nal Kalchbrenner,Ilya Sutskever,Timothy P. Lillicrap,Madeleine Leach,Koray Kavukcuoglu,Thore Graepel,Demis Hassabis +19 more
TLDR
Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.Abstract:
The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of stateof-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.read more
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Journal ArticleDOI
Outracing champion Gran Turismo drivers with deep reinforcement learning
Peter R. Wurman,Sam C. Barrett,Kenta Kawamoto,James MacGlashan,Kaushik Subramanian,Tom Walsh,Roberto Capobianco,Alisa Devlic,Franziska Eckert,Florian Fuchs,Leilani H. Gilpin,Piyush Khandelwal,Varun J. Kompella,HaoChih Lin,Patrick MacAlpine,Declan Danesh Oller,Takuma Seno,Craig Sherstan,Mick Thomure,Houmehr Aghabozorgi,Leon Barrett,Rory Douglas,Dion J. Whitehead,Peter Dürr,Peter Stone,Michael Spranger,Hiroaki Kitano +26 more
TL;DR: In this article , the authors describe how they trained agents for Gran Turismo that can compete with the world's best e-sports drivers, and demonstrate the possibilities and challenges of using these techniques to control complex dynamical systems in domains where agents must respect imprecisely defined human norms.
Posted Content
A Review of Cooperative Multi-Agent Deep Reinforcement Learning
TL;DR: This review article has mostly focused on recent papers on Multi-Agent Reinforcement Learning (MARL) than the older papers, unless it was necessary, and discussed some new emerging research areas in MARL along with the relevant recent papers.
Journal ArticleDOI
A Transfer Learning Approach for Microstructure Reconstruction and Structure-property Predictions
Xiaolin Li,Yichi Zhang,He Zhao,Craig Burkhart,L. Catherine Brinson,L. Catherine Brinson,Wei Chen +6 more
TL;DR: In this paper, a transfer learning-based approach for microstructure reconstruction and structure-property predictions is presented, which is applicable to a wide range of material systems and incorporates an encoder-decoder process and feature-matching optimization using a deep CNN.
Journal ArticleDOI
The Molecular Industrial Revolution: Automated Synthesis of Small Molecules.
Melanie Trobe,Martin D. Burke +1 more
TL;DR: Today the authors are poised for a transition from the highly customized crafting of specific molecular targets by hand to the increasingly general and automated assembly of different types of molecules with the push of a button.
Book ChapterDOI
SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing
TL;DR: An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
References
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ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
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.