LETTER doi:nature14236 Human-level control through deep reinforcement learning

First Page		Document Content
Date: 2016-01-26 06:53:21 Artificial intelligence Machine learning Computational neuroscience Applied mathematics Cybernetics Q-learning Artificial neural networks Reinforcement learning Convolutional neural network DQN Markov decision process Supervised learning		LETTER doi:nature14236 Human-level control through deep reinforcement learning Add to Reading List Source URL: storage.googleapis.com Download Document from Source Website File Size: 4,39 MB Share Document on Facebook

	Journal of Computational Neuroscience 18, 105–121, 2005 c 2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands. A Cellular Mechanism for Graded Persistent Activity in a Model Neuron and Its Im DocID: 1vcJi - View Document
	munich Bernstein Center for Computational Neuroscience Munich DocID: 1vcv8 - View Document
	Blending computational and experimental neuroscience DocID: 1v9kZ - View Document
	Florentin Wörgötter Bernstein Center for Computational Neuroscience Göttingen Florentin Wörgötter studied biology and mathematics at the University of Düsseldorf, Germany. He received a Ph.D. degree, studying DocID: 1v914 - View Document
	Nonlinear synaptic interaction as a computational resource in the Neural Engineering Framework Andreas Stöckel, Aaron R. Voelker, Chris Eliasmith Centre for Theoretical Neuroscience, University of Waterloo {astoecke, ar DocID: 1uuia - View Document