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
Conditions for reservoir computing performance using semiconductor lasers with delayed optical feedback.
TL;DR: A particularly attractive all-optical system using optical information injection into a semiconductor laser with delayed feedback is studied, and it is found that for partial injection locking the authors achieve a good combination of consistency and memory.
Proceedings Article
Evolution-Guided Policy Gradient in Reinforcement Learning
Shauharda Khadka,Kagan Tumer +1 more
TL;DR: Evolutionary Reinforcement Learning (ERL), a hybrid algorithm that leverages the population of an EA to provide diversified data to train an RL agent, and reinserts the RL agent into theEA population periodically to inject gradient information into the EA.
Journal ArticleDOI
Machine Learning of Explicit Order Parameters: From the Ising Model to SU(2) Lattice Gauge Theory
TL;DR: A procedure for reconstructing the decision function of an artificial neural network as a simple function of the input, provided the decisionfunction is sufficiently symmetric.
Journal ArticleDOI
The security of machine learning in an adversarial setting: A survey
TL;DR: This work presents a comprehensive overview of the investigation of the security properties of ML algorithms under adversarial settings, and analyze the ML security model to develop a blueprint for this interdisciplinary research area.
Journal ArticleDOI
Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform
TL;DR: Using data from a digital platform, artificial intelligence is surpassing human performance in a growing number of domains but there is limited evidence of its economic effects.
References
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