Magnetic control of tokamak plasmas through deep reinforcement learning von Degrave, Jonas, Felici, Federico, Buchli, Jonas, Neunert, Michael, Tracey, Brendan, Carpanese, Francesco, Ewalds, Timo, Hafner, Roland, Abdolmaleki, Abbas, de las Casas, Diego, Donner, Craig, Fritz, Leslie, Galperti, Cristian, Huber, Andrea, Keeling, James, Tsimpoukelli, Maria, Kay, Jackie, Merle, Antoine, Moret, Jean-Marc, Noury, Seb, Pesamosca, Federico, Pfau, David, Sauter, Olivier, Sommariva, Cristian, Coda, Stefano, Duval, Basil, Fasoli, Ambrogio, Kohli, Pushmeet, Kavukcuoglu, Koray, Hassabis, Demis, Riedmiller, Martin

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Autonomous Optimization of Targeted Stimulation of Neuronal Networks von Kumar, Sreedhar S, Wülfing, Jan, Okujeni, Samora, Boedecker, Joschka, Riedmiller, Martin, Egert, Ulrich

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Reinforcement learning in feedback control: Challenges and benchmarks from technical process control von Hafner, Roland, Riedmiller, Martin

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Neural Fitted Q Iteration – First Experiences with a Data Efficient Neural Reinforcement Learning Method von Riedmiller, Martin

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Calculating the perfect match : An efficient and accurate approach for Robot-Self-localization von LAUER, Martin, LANGE, Sascha, RIEDMILLER, Martin

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Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks von Dosovitskiy, Alexey, Fischer, Philipp, Springenberg, Jost Tobias, Riedmiller, Martin, Brox, Thomas

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Advanced supervised learning in multi-layer perceptrons — From backpropagation to adaptive learning algorithms von Riedmiller, Martin

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Distributed policy search reinforcement learning for job-shop scheduling tasks von Gabel, Thomas, Riedmiller, Martin

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Human-level control through deep reinforcement learning von Mnih, Volodymyr, Kavukcuoglu, Koray, Silver, David, Rusu, Andrei A., Veness, Joel, Bellemare, Marc G., Graves, Alex, Riedmiller, Martin, Fidjeland, Andreas K., Ostrovski, Georg, Petersen, Stig, Beattie, Charles, Sadik, Amir, Antonoglou, Ioannis, King, Helen, Kumaran, Dharshan, Wierstra, Daan, Legg, Shane, Hassabis, Demis

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Reinforcement learning for robot soccer von Riedmiller, Martin, Gabel, Thomas, Hafner, Roland, Lange, Sascha

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Faster sorting algorithms discovered using deep reinforcement learning von Mankowitz, Daniel J., Michi, Andrea, Zhernov, Anton, Gelmi, Marco, Selvi, Marco, Paduraru, Cosmin, Leurent, Edouard, Iqbal, Shariq, Lespiau, Jean-Baptiste, Ahern, Alex, Köppe, Thomas, Millikin, Kevin, Gaffney, Stephen, Elster, Sophie, Broshear, Jackson, Gamble, Chris, Milan, Kieran, Tung, Robert, Hwang, Minjae, Cemgil, Taylan, Barekatain, Mohammadamin, Li, Yujia, Mandhane, Amol, Hubert, Thomas, Schrittwieser, Julian, Hassabis, Demis, Kohli, Pushmeet, Riedmiller, Martin, Vinyals, Oriol, Silver, David

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Adaptive long-term control of biological neural networks with Deep Reinforcement Learning von Wülfing, Jan M., Kumar, Sreedhar S., Boedecker, Joschka, Riedmiller, Martin, Egert, Ulrich

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Distributed policy search reinforcement learning for job-shop scheduling tasks: Applications of computational intelligence for design and operations decisions in manufacturing von GABEL, Thomas, RIEDMILLER, Martin

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Reinforcement learning in feedback control von Hafner, Roland, Riedmiller, Martin

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Towards practical reinforcement learning for tokamak magnetic control von Tracey, Brendan D., Michi, Andrea, Chervonyi, Yuri, Davies, Ian, Paduraru, Cosmin, Lazic, Nevena, Felici, Federico, Ewalds, Timo, Donner, Craig, Galperti, Cristian, Buchli, Jonas, Neunert, Michael, Huber, Andrea, Evens, Jonathan, Kurylowicz, Paula, Mankowitz, Daniel J., Riedmiller, Martin

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Reinforcement learning in feedback control Challenges and benchmarks from technical process control: Special Issue on Empirical Evaluations in Reinforcement Learning von HAFNER, Roland, RIEDMILLER, Martin

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Deep auto-encoder neural networks in reinforcement learning von Lange, Sascha, Riedmiller, Martin

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10 Steps and Some Tricks to Set up Neural Reinforcement Controllers von Riedmiller, Martin

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Learn to Swing Up and Balance a Real Pole Based on Raw Visual Input Data von Mattner, Jan, Lange, Sascha, Riedmiller, Martin

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The Cooperative Driver: Multi-Agent Learning for Preventing Traffic Jams von Gabel, Thomas, Riedmiller, Martin

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