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
Data mining and machine learning methods for sustainable smart cities traffic classification: a survey
TL;DR: Challenges and recommendations for SSC network traffic classification with the dataset of features are presented and some well-known and most used datasets with details statistical features are described.
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Machine Learning for industrial applications: A comprehensive literature review
TL;DR: This paper deals with industrial applications of ML techniques, intending to clarify the real potentialities, as well as potential flaws, of ML algorithms applied to operation management, and a comprehensive review is presented and organized in a way that should facilitate the orientation of practitioners in this field.
Model-Based Reinforcement Learning via Meta-Policy Optimization
TL;DR: This work proposes Model-Based Meta-Policy-Optimization (MB-MPO), an approach that foregoes the strong reliance on accurate learned dynamics models and uses an ensemble of learned dynamic models to create a policy that can quickly adapt to any model in the ensemble with one policy gradient step.
Journal ArticleDOI
Jamming transition as a paradigm to understand the loss landscape of deep neural networks
Mario Geiger,Stefano Spigler,Stéphane d'Ascoli,Stéphane d'Ascoli,Levent Sagun,Levent Sagun,Marco Baity-Jesi,Giulio Biroli,Giulio Biroli,Matthieu Wyart +9 more
TL;DR: It is argued that in fully connected deep networks a phase transition delimits the over- and underparametrized regimes where fitting can or cannot be achieved, and observed that the ability of fully connected networks to fit random data is independent of their depth, an independence that appears to also hold for real data.
Journal ArticleDOI
Efficient Processing of Deep Neural Networks
TL;DR: This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs).
References
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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.
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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.