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
Recent Advances and Challenges in Task-oriented Dialog System
TL;DR: Three critical topics for task-oriented dialog systems are discussed: improving data efficiency to facilitate dialog modeling in low-resource settings, modeling multi-turn dynamics for dialog policy learning to achieve better task-completion performance, and integrating domain ontology knowledge into the dialog model.
Journal ArticleDOI
Deep learning and model predictive control for self-tuning mode-locked lasers
TL;DR: This work demonstrates the first integration of a deep-learning architecture with model predictive control (MPC) in order to self-tune a mode-locked fiber laser and builds a dynamical model of the laser and appropriate control law for maintaining robust, high-energy pulses despite a stochastically drifting birefringence.
Proceedings ArticleDOI
Deep reinforcement learning approaches for process control
TL;DR: It is shown that if reward hypothesis functions are formulated properly, they can be used for industrial process control if they are used forindustrial process control.
Proceedings ArticleDOI
Wuji: automatic online combat game testing using evolutionary deep reinforcement learning
Yan Zheng,Changjie Fan,Xiaofei Xie,Ting Su,Lei Ma,Jianye Hao,Zhaopeng Meng,Yang Liu,Ruimin Shen,Yingfeng Chen +9 more
TL;DR: Wuji is proposed, an on-the-fly game testing framework, which leverages evolutionary algorithms, DRL and multi-objective optimization to perform automatic game testing and demonstrates the effectiveness of Wuji in exploring space and detecting bugs.
Posted Content
How deep is knowledge tracing
TL;DR: This article argues that while DKT is a powerful, useful, general-purpose framework for modeling student learning, its gains do not come from the discovery of novel representations---the fundamental advantage of deep learning, and hypothesizes four forms of regularity that BKT fails to exploit.
References
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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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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
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