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
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Journal ArticleDOI
De novo composite design based on machine learning algorithm
TL;DR: This work optimization a large-scale system not tractable by an exhaustive brute force approach and shows that it is a promising tool towards composite design offers a new perspective in the exploration of design spaces and accelerating the discovery of new functional, customizable composites.
Journal ArticleDOI
Active learning machine learns to create new quantum experiments
Alexey A. Melnikov,Hendrik Poulsen Nautrup,Mario Krenn,Mario Krenn,Vedran Dunjko,Markus Tiersch,Anton Zeilinger,Anton Zeilinger,Hans J. Briegel,Hans J. Briegel +9 more
TL;DR: An autonomous learning model is presented which learns to design complex photonic quantum experiments that produce high-dimensional entangled multiphoton states, which are of high interest in modern quantum experiments and improves the efficiency of their realization.
Journal ArticleDOI
Theories of Error Back-Propagation in the Brain.
TL;DR: This review article summarises recently proposed theories on how neural circuits in the brain could approximate the error back-propagation algorithm used by artificial neural networks and provides insights on how brain networks might be organised such that modification of synaptic weights on multiple levels of cortical hierarchy leads to improved performance on tasks.
Proceedings ArticleDOI
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
TL;DR: This work poses a cooperative ‘image guessing’ game between two agents who communicate in natural language dialog so that Q-BOT can select an unseen image from a lineup of images and shows the emergence of grounded language and communication among ‘visual’ dialog agents with no human supervision.
Journal ArticleDOI
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems
Laura von Rueden,Sebastian Mayer,Katharina Beckh,Bogdan Georgiev,Sven Giesselbach,Raoul Heese,Birgit Kirsch,Julius Pfrommer,Annika Pick,Rajkumar Ramamurthy,Michal Walczak,Jochen Garcke,Christian Bauckhage,Jannis Schuecker +13 more
TL;DR: A definition and proposed concept for informed machine learning is provided, which illustrates its building blocks and distinguishes it from conventional machine learning, and a taxonomy is introduced that serves as a classification framework forinformed machine learning approaches.
References
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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
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