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We present a novel definition of the reinforcement learning state, actions and reward function that allows a deep Q-network (DQN) to learn to control an optimization hyperparameter.
Minimization of functions having lipschitz continuous first partial derivatives
Larry Armijo · 1966
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A nonmonotone line search technique for newton’s method
Luigi Grippo, Francesco Lampariello, and Stephano Lucidi · 1986
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Q-learning
Christopher JCH Watkins and Peter Dayan · 1992
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Reinforcement learning for robots using neural networks
Long-Ji Lin · 1993
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A direct adaptive method for faster backpropagation learning: The rprop algorithm
Martin Riedmiller and Heinrich Braun · 1993
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Ant-q: A reinforcement learning approach to the traveling salesman problem
Marco Dorigo and LM Gambardella · 1995
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Temporal difference learning and td-gammon
Gerald Tesauro · 1995
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Learning evaluation functions for global optimization and boolean satisfiability
Justin A Boyan and Andrew W Moore · 1998
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 1998
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Global search in combinatorial optimization using reinforcement learning algorithms
Victor V Miagkikh and William F Punch III · 1999
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Machine learning for subproblem selection
Robert Moll, Theodore J Perkins, and Andrew G Barto · 2000
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Neural fitted q iteration–first experiences with a data efficient neural reinforcement learning method
Martin Riedmiller · 2005
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Numerical optimization
Jorge Nocedal and Stephen Wright · 2006
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Optimization on a budget: A reinforcement learning approach
Paul L Ruvolo, Ian Fasel, and Javier R Movellan · 2009
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, and Tara N Sainath · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Efficient backprop
Yann A LeCun, Léon Bottou, Genevieve B Orr, and Klaus-Robert Müller · 2012
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Lecture 6.5-rmsprop
Tijmen Tieleman and Geoffrey Hinton · 2012
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A nonmonotone learning rate strategy for sgd training of deep neural networks
Nitish Shirish Keskar and George Saon · 2015
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A unified architecture for natural language processing: Deep neural networks with multitask learning
Ronan Collobert and Jason Weston · 2008
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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