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
A review On reinforcement learning: Introduction and applications in industrial process control
Rui Nian,Jinfeng Liu,Biao Huang +2 more
TL;DR: An overview of RL along with tutorials for practitioners who are interested in implementing RL solutions into process control applications and a summary of RL’s potential advantages and disadvantages are provided.
Journal ArticleDOI
Deep Learning: A Rapid and Efficient Route to Automatic Metasurface Design.
TL;DR: This work designs a triple‐band absorber using the REACTIVE method, where a deep learning model computes the metasurface structure automatically through inputting the desired absorption rate.
Proceedings Article
On the Power of Over-parametrization in Neural Networks with Quadratic Activation
Simon S. Du,Jason D. Lee +1 more
TL;DR: The authors showed that over-parametrization enables local search algorithms to find a globally optimal solution for general smooth and convex loss functions, and showed that the solution also generalizes well if the data is sampled from a regular distribution such as Gaussian.
Journal ArticleDOI
Predicting the Thermodynamic Stability of Solids Combining Density Functional Theory and Machine Learning
TL;DR: In this paper, a large scale benchmark of machine learning methods for the prediction of the thermodynamic stability of solids is presented, which includes all possible perovskite and anti-perovskiite crystals that can be generated with elements from hydrogen to bismuth, excluding rare gases and lanthanides.
Proceedings ArticleDOI
Model-free Deep Reinforcement Learning for Urban Autonomous Driving
TL;DR: This paper proposes a framework to enable model-free deep reinforcement learning in challenging urban autonomous driving scenarios, and designs a specific input representation and uses visual encoding to capture the low-dimensional latent states.
References
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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
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