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
Optoelectronic Synapse Based on IGZO-Alkylated Graphene Oxide Hybrid Structure
Jia Sun,Jia Sun,Seyong Oh,Yongsuk Choi,Seunghwan Seo,Min Jun Oh,Min Hwan Lee,Won Bo Lee,Pil J. Yoo,Jeong Ho Cho,Jin-Hong Park +10 more
TL;DR: Owing to this enhancement of synaptic operation, the recognition rates for the Modified National Institute of Standards and Technology digit patterns improve from 36% and 49% to 50% and 62% in artificial neural networks using long‐term potentiation/depression characteristics with 20 and 100 weight states, respectively.
Journal ArticleDOI
Multi-task Deep Reinforcement Learning with PopArt
Matteo Hessel,Hubert Soyer,Lasse Espeholt,Wojciech Marian Czarnecki,Simon Schmitt,Hado van Hasselt +5 more
TL;DR: This work proposes to automatically adapt the contribution of each task to the agent’s updates, so that all tasks have a similar impact on the learning dynamics, and learns a single trained policy that exceeds median human performance on this multi-task domain.
Proceedings ArticleDOI
A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs
TL;DR: A novel DRL-based framework for power-efficient resource allocation in cloud RANs is presented, which can achieve significant power savings while meeting user demands, and it can well handle highly dynamic cases.
Journal ArticleDOI
Knowledge-Defined Networking
Albert Mestres,Alberto Rodriguez-Natal,Josep Carner,Pere Barlet-Ros,Eduard Alarcon,Marc Solé,Victor Muntes,David E. Meyer,Sharon Barkai,Mike J. Hibbett,Giovani Estrada,Khaldun Ma`ruf,Florin Coras,Vina Ermagan,Hugo Latapie,Chris Cassar,John Evans,Fabio Maino,Jean Walrand,Albert Cabellos +19 more
TL;DR: In this article, the authors explore the reasons for the lack of adoption and posit that the rise of two recent paradigms: Software-Defined Networking (SDN) and Network Analytics (NA), will facilitate the adoption of AI techniques in the context of network operation and control.
Book ChapterDOI
Learning Heuristics for the TSP by Policy Gradient
TL;DR: The neural combinatorial optimization framework is extended to solve the traveling salesman problem (TSP) and the performance of the proposed framework alone is generally as good as high performance heuristics (OR-Tools).
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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