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
Citations
More filters
Journal ArticleDOI
Deep Learning: A Primer for Radiologists
Gabriel Chartrand,Phillip M. Cheng,Eugene Vorontsov,Michal Drozdzal,Simon Turcotte,Chris Pal,Samuel Kadoury,An Tang +7 more
TL;DR: The key concepts of deep learning for clinical radiologists are reviewed, technical requirements are discussed, emerging applications in clinical radiology are described, and limitations and future directions in this field are outlined.
Journal ArticleDOI
Machine learning & artificial intelligence in the quantum domain: a review of recent progress.
TL;DR: In this article, the authors describe the main ideas, recent developments and progress in a broad spectrum of research investigating ML and AI in the quantum domain, and discuss the fundamental issue of quantum generalizations of learning and AI concepts.
Posted Content
StarCraft II: A New Challenge for Reinforcement Learning
Oriol Vinyals,Timo Ewalds,Sergey Bartunov,Petko Georgiev,Alexander Vezhnevets,Michelle Yeo,Alireza Makhzani,Heinrich Küttler,John P. Agapiou,Julian Schrittwieser,John Quan,Stephen Gaffney,Stig Petersen,Karen Simonyan,Tom Schaul,Hado van Hasselt,David Silver,Timothy P. Lillicrap,Kevin Calderone,Paul Keet,Anthony Brunasso,David Lawrence,Anders Ekermo,Jacob Repp,Rodney Tsing +24 more
TL;DR: This paper introduces SC2LE (StarCraft II Learning Environment), a reinforcement learning environment based on the StarCraft II game that offers a new and challenging environment for exploring deep reinforcement learning algorithms and architectures and gives initial baseline results for neural networks trained from this data to predict game outcomes and player actions.
Journal ArticleDOI
Mastering Atari, Go, chess and shogi by planning with a learned model
Julian Schrittwieser,Ioannis Antonoglou,Thomas Hubert,Karen Simonyan,Laurent Sifre,Simon Schmitt,Arthur Guez,Edward Lockhart,Demis Hassabis,Thore Graepel,Timothy P. Lillicrap,David Silver +11 more
TL;DR: MuZero as discussed by the authors is a reinforcement learning algorithm that combines a tree-based search with a learned model to achieve state-of-the-art performance in high-performance planning and visually complex domains.
Journal ArticleDOI
Machine Learning: Algorithms, Real-World Applications and Research Directions
TL;DR: In this paper, the authors present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application and highlight the challenges and potential research directions based on their study.
References
More filters
Proceedings Article
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.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
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.
Book
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.