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
Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations
TL;DR: In this paper, the authors proposed a new method for solving high-dimensional fully nonlinear second-order partial differential equations (PDE), which can be used to sample from highdimensional nonlinear expectations.
Proceedings Article
Deeply AggreVaTeD: differentiable imitation learning for sequential prediction
TL;DR: AggreVaTeD as discussed by the authors is an extension of the imitation learning (IL) approach of Ross & Bagnell (2014), which allows to use expressive differentiable policy representations such as deep networks, while leveraging training-time oracles to achieve faster and more accurate solutions.
Posted Content
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
Sriram Srinivasan,Marc Lanctot,Vinicius Zambaldi,Julien Perolat,Karl Tuyls,Rémi Munos,Michael Bowling +6 more
TL;DR: This paper examines the role of policy gradient and actor-critic algorithms in partially-observable multiagent environments and relates them to a foundation of regret minimization and multiagent learning techniques for the one-shot and tabular cases, leading to previously unknown convergence guarantees.
Journal ArticleDOI
Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review
Anna Markella Antoniadi,Yuhan Du,Yasmine Guendouz,Lan Wei,Claudia Mazo,Brett A. Becker,Catherine Mooney +6 more
TL;DR: An overall distinct lack of application of XAI is found in the context of CDSS and, in particular, a lack of user studies exploring the needs of clinicians is found.
Journal ArticleDOI
2D Material Based Synaptic Devices for Neuromorphic Computing
TL;DR: A comprehensive review of synaptic devices based on 2D materials is provided, including the advantages of2D materials and heterostructures, various robust multifunctional 2D synaptic devices, and associated neuromorphic applications.
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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