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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Proceedings ArticleDOI
Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents (Extended Abstract).
Posted Content
Multiagent Cooperation and Competition with Deep Reinforcement Learning
Ardi Tampuu,Tambet Matiisen,Dorian Kodelja,Ilya Kuzovkin,Kristjan Korjus,Juhan Aru,Jaan Aru,Raul Vicente +7 more
TL;DR: In this article, the Deep Q-Learning Network architecture was extended to multiagent environments and investigated how two agents controlled by independent deep Q-networks interact in the classic videogame Pong.
Book ChapterDOI
Towards Explainable Artificial Intelligence
TL;DR: This introductory paper presents recent developments and applications in deep learning, and makes a plea for a wider use of explainable learning algorithms in practice.
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Machine learning \& artificial intelligence in the quantum domain
Vedran Dunjko,Hans J. Briegel +1 more
TL;DR: The main ideas, recent developments and progress are described in a broad spectrum of research investigating ML and AI in the quantum domain, investigating how results and techniques from one field can be used to solve the problems of the other.
Posted Content
Stochastic Neural Networks for Hierarchical Reinforcement Learning
TL;DR: This work proposes a general framework that first learns useful skills in a pre-training environment, and then leverages the acquired skills for learning faster in downstream tasks, and uses Stochastic Neural Networks combined with an information-theoretic regularizer to efficiently pre-train a large span of skills.
References
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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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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.