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
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Posted Content
Is multiagent deep reinforcement learning the answer or the question? A brief survey
TL;DR: This article provides a clear overview of current multiagent deep reinforcement learning (MDRL) literature and provides guidelines to complement this emerging area by showcasing examples on how methods and algorithms from DRL and multiagent learning (MAL) have helped solve problems in MDRL and providing general lessons learned from these works.
Journal ArticleDOI
Computational prediction of chemical reactions: current status and outlook.
Ola Engkvist,Per-Ola Norrby,Nidhal Selmi,Yu-hong Lam,Zhengwei Peng,Edward C. Sherer,Willi Amberg,Thomas Erhard,Lynette A. Smyth +8 more
TL;DR: Important parts of the field of computational prediction of chemical reactions are discussed, with a focus on utilizing reaction data to build predictive models, the existing programs for synthesis prediction, and usage of quantum mechanics and molecular mechanics to explore chemical reactions.
Proceedings ArticleDOI
Learning Channel-Wise Interactions for Binary Convolutional Neural Networks
TL;DR: Extensive experiments show that the CI-BCNN outperforms the state-of-the-art binary convolutional neural networks with less computational and storage cost and imposes channel-wise priors on the intermediate feature maps through the interacted bitcount function.
Journal ArticleDOI
Assessing the impact of generative AI on medicinal chemistry.
W. Patrick Walters,Murcko Mark A +1 more
TL;DR: The synthesis and testing of molecules derived from a generative model, a variation on the de novo design programs that were in vogue during the 1990s and early 2000s, and the ultimate value of generative models will be demonstrated through the synthesis and biological evaluation of the novel molecules they identify are reported.
Journal ArticleDOI
In-memory Learning with Analog Resistive Switching Memory: A Review and Perspective
Yue Xi,Bin Gao,Jianshi Tang,An Chen,Meng-Fan Chang,Xiaobo Sharon Hu,Jan Van der Spiegel,He Qian,Huaqiang Wu +8 more
TL;DR: This article defines the main figures of merit (FoMs) of analog RSM hardware including the basic device characteristics, hardware algorithms, and the corresponding mapping methods for device arrays, as well as the architecture and circuit design considerations for neural networks.
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
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.