Martin A. Riedmiller

206 papers A* 24A 4B 16C 17Misc 11Journal 88Unranked 41

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	DemoStart: Demonstration-Led Auto-Curriculum Applied to Sim-to-Real with Multi-Fingered Robots. ICRA Maria Bauzá, Jose Enriaue Chen, Valentin Dalibard, Nimrod Gileadi, Roland Hafner, Murilo F. Martins, Joss Moore, Rugile Pevceviciute, Antoine Laurens, Dushyant Rao, Martina Zambelli, Martin A. Riedmiller, Jon Scholz, Konstantinos Bousmalis, Francesco Nori, Nicolas Heess
2025	J	`jnl`	Improving cosmological reach of a gravitational wave observatory using Deep Loop Shaping. CoRR Jonas Buchli, Brendan D. Tracey, Tomislav Andric, Christopher Wipf, Yu Him Justin Chiu, Matthias Lochbrunner, Craig Donner, Rana X. Adhikari, Jan Harms, Iain Barr, Roland Hafner, Andrea Huber, Abbas Abdolmaleki, Charlie Beattie, Joseph Betzwieser, Serkan Cabi, Jonas Degrave, Yuzhu Dong, Leslie Fritz, Anchal Gupta, Oliver Groth, Sandy H. Huang, Tamara Norman, Hannah Openshaw, Jameson Rollins, Greg Thornton, George Van Den Driessche, Markus Wulfmeier, Pushmeet Kohli, Martin A. Riedmiller, LIGO Instrument Team
2025	A*	`conf`	Learning from negative feedback, or positive feedback or both. ICLR Abbas Abdolmaleki, Bilal Piot, Bobak Shahriari, Jost Tobias Springenberg, Tim Hertweck, Michael Bloesch, Rishabh Joshi, Thomas Lampe, Junhyuk Oh, Nicolas Heess, Jonas Buchli, Martin A. Riedmiller
2025	J	`jnl`	NFQ2.0: The CartPole Benchmark Revisited. CoRR Sascha Lange, Roland Hafner, Martin A. Riedmiller
2025	J	`jnl`	Value from Observations: Towards Large-Scale Imitation Learning via Self-Improvement. CoRR Michael Bloesch, Markus Wulfmeier, Philemon Brakel, Todor Davchev, Martina Zambelli, Jost Tobias Springenberg, Abbas Abdolmaleki, William F. Whitney, Nicolas Heess, Roland Hafner, Martin A. Riedmiller
2024	J	`jnl`	DemoStart: Demonstration-led auto-curriculum applied to sim-to-real with multi-fingered robots. CoRR Maria Bauzá, José Enrique Chen, Valentin Dalibard, Nimrod Gileadi, Roland Hafner, Murilo F. Martins, Joss Moore, Rugile Pevceviciute, Antoine Laurens, Dushyant Rao, Martina Zambelli, Martin A. Riedmiller, Jon Scholz, Konstantinos Bousmalis, Francesco Nori, Nicolas Heess
2024	J	`jnl`	Game On: Towards Language Models as RL Experimenters. CoRR Jingwei Zhang, Thomas Lampe, Abbas Abdolmaleki, Jost Tobias Springenberg, Martin A. Riedmiller
2024	A*	`conf`	Imitating Language via Scalable Inverse Reinforcement Learning. NeurIPS Markus Wulfmeier, Michael Bloesch, Nino Vieillard, Arun Ahuja, Jörg Bornschein, Sandy H. Huang, Artem Sokolov, Matt Barnes, Guillaume Desjardins, Alex Bewley, Sarah Bechtle, Jost Tobias Springenberg, Nikola Momchev, Olivier Bachem, Matthieu Geist, Martin A. Riedmiller
2024	J	`jnl`	Imitating Language via Scalable Inverse Reinforcement Learning. CoRR Markus Wulfmeier, Michael Bloesch, Nino Vieillard, Arun Ahuja, Jörg Bornschein, Sandy H. Huang, Artem Sokolov, Matt Barnes, Guillaume Desjardins, Alex Bewley, Sarah Maria Elisabeth Bechtle, Jost Tobias Springenberg, Nikola Momchev, Olivier Bachem, Matthieu Geist, Martin A. Riedmiller
2024	Misc	`conf`	Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning. CoRL Dhruva Tirumala, Markus Wulfmeier, Ben Moran, Sandy H. Huang, Jan Humplik, Guy Lever, Tuomas Haarnoja, Leonard Hasenclever, Arunkumar Byravan, Nathan Batchelor, Neil Sreendra, Kushal Patel, Marlon Gwira, Francesco Nori, Martin A. Riedmiller, Nicolas Heess
2024	J	`jnl`	Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning. CoRR Dhruva Tirumala, Markus Wulfmeier, Ben Moran, Sandy H. Huang, Jan Humplik, Guy Lever, Tuomas Haarnoja, Leonard Hasenclever, Arunkumar Byravan, Nathan Batchelor, Neil Sreendra, Kushal Patel, Marlon Gwira, Francesco Nori, Martin A. Riedmiller, Nicolas Heess
2024	A*	`conf`	Mastering Stacking of Diverse Shapes with Large-Scale Iterative Reinforcement Learning on Real Robots. ICRA Thomas Lampe, Abbas Abdolmaleki, Sarah Bechtle, Sandy H. Huang, Jost Tobias Springenberg, Michael Bloesch, Oliver Groth, Roland Hafner, Tim Hertweck, Michael Neunert, Markus Wulfmeier, Jingwei Zhang, Francesco Nori, Nicolas Heess, Martin A. Riedmiller
2024	A*	`conf`	Offline Actor-Critic Reinforcement Learning Scales to Large Models. ICML Jost Tobias Springenberg, Abbas Abdolmaleki, Jingwei Zhang, Oliver Groth, Michael Bloesch, Thomas Lampe, Philemon Brakel, Sarah Bechtle, Steven Kapturowski, Roland Hafner, Nicolas Heess, Martin A. Riedmiller
2024	J	`jnl`	Offline Actor-Critic Reinforcement Learning Scales to Large Models. CoRR Jost Tobias Springenberg, Abbas Abdolmaleki, Jingwei Zhang, Oliver Groth, Michael Bloesch, Thomas Lampe, Philemon Brakel, Sarah Bechtle, Steven Kapturowski, Roland Hafner, Nicolas Heess, Martin A. Riedmiller
2024	J	`jnl`	Preference Optimization as Probabilistic Inference. CoRR Abbas Abdolmaleki, Bilal Piot, Bobak Shahriari, Jost Tobias Springenberg, Tim Hertweck, Rishabh Joshi, Junhyuk Oh, Michael Bloesch, Thomas Lampe, Nicolas Heess, Jonas Buchli, Martin A. Riedmiller
2024	J	`jnl`	Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning. CoRR Mohak Bhardwaj, Thomas Lampe, Michael Neunert, Francesco Romano, Abbas Abdolmaleki, Arunkumar Byravan, Markus Wulfmeier, Martin A. Riedmiller, Jonas Buchli
2024	—	`conf`	Real-world fluid directed rigid body control via deep reinforcement learning. L4DC Mohak Bhardwaj, Thomas Lampe, Michael Neunert, Francesco Romano, Abbas Abdolmaleki, Arunkumar Byravan, Markus Wulfmeier, Martin A. Riedmiller, Jonas Buchli
2024	A*	`conf`	Replay across Experiments: A Natural Extension of Off-Policy RL. ICLR Dhruva Tirumala, Thomas Lampe, José Enrique Chen, Tuomas Haarnoja, Sandy H. Huang, Guy Lever, Ben Moran, Tim Hertweck, Leonard Hasenclever, Martin A. Riedmiller, Nicolas Heess, Markus Wulfmeier
2024	J	`jnl`	RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation. Trans. Mach. Learn. Res. Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Manon Devin, Alex X. Lee, Maria Bauzá Villalonga, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, Antoine Laurens, Claudio Fantacci, Valentin Dalibard, Martina Zambelli, Murilo Fernandes Martins, Rugile Pevceviciute, Michiel Blokzijl, Misha Denil, Nathan Batchelor, Thomas Lampe, Emilio Parisotto, Konrad Zolna, Scott E. Reed, Sergio Gómez Colmenarejo, Jon Scholz, Abbas Abdolmaleki, Oliver Groth, Jean-Baptiste Regli, Oleg Sushkov, Thomas Rothörl, José Enrique Chen, Yusuf Aytar, Dave Barker, Joy Ortiz, Martin A. Riedmiller, Jost Tobias Springenberg, Raia Hadsell, Francesco Nori, Nicolas Heess
2023	J	`jnl`	A Generalist Dynamics Model for Control. CoRR Ingmar Schubert, Jingwei Zhang, Jake Bruce, Sarah Bechtle, Emilio Parisotto, Martin A. Riedmiller, Jost Tobias Springenberg, Arunkumar Byravan, Leonard Hasenclever, Nicolas Heess
2023	J	`jnl`	Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning. CoRR Cristina Pinneri, Sarah Bechtle, Markus Wulfmeier, Arunkumar Byravan, Jingwei Zhang, William F. Whitney, Martin A. Riedmiller
2023	J	`jnl`	Faster sorting algorithms discovered using deep reinforcement learning. Nat. Daniel J. Mankowitz, Andrea Michi, Anton Zhernov, Marco Gelmi, Marco Selvi, Cosmin Paduraru, Edouard Leurent, Shariq Iqbal, Jean-Baptiste Lespiau, Alex Ahern, Thomas Köppe, Kevin Millikin, Stephen Gaffney, Sophie Elster, Jackson Broshear, Chris Gamble, Kieran Milan, Robert Tung, Minjae Hwang, A. Taylan Cemgil, Mohammadamin Barekatain, Yujia Li, Amol Mandhane, Thomas Hubert, Julian Schrittwieser, Demis Hassabis, Pushmeet Kohli, Martin A. Riedmiller, Oriol Vinyals, David Silver
2023	J	`jnl`	Less is more - the Dispatcher/ Executor principle for multi-task Reinforcement Learning. CoRR Martin A. Riedmiller, Tim Hertweck, Roland Hafner
2023	J	`jnl`	Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains. CoRR Jingwei Zhang, Jost Tobias Springenberg, Arunkumar Byravan, Leonard Hasenclever, Abbas Abdolmaleki, Dushyant Rao, Nicolas Heess, Martin A. Riedmiller
2023	J	`jnl`	Mastering Stacking of Diverse Shapes with Large-Scale Iterative Reinforcement Learning on Real Robots. CoRR Thomas Lampe, Abbas Abdolmaleki, Sarah Bechtle, Sandy H. Huang, Jost Tobias Springenberg, Michael Bloesch, Oliver Groth, Roland Hafner, Tim Hertweck, Michael Neunert, Markus Wulfmeier, Jingwei Zhang, Francesco Nori, Nicolas Heess, Martin A. Riedmiller
2023	J	`jnl`	Policy composition in reinforcement learning via multi-objective policy optimization. CoRR Shruti Mishra, Ankit Anand, Jordan Hoffmann, Nicolas Heess, Martin A. Riedmiller, Abbas Abdolmaleki, Doina Precup
2023	J	`jnl`	Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World. CoRR Nico Gürtler, Felix Widmaier, Cansu Sancaktar, Sebastian Blaes, Pavel Kolev, Stefan Bauer, Manuel Wüthrich, Markus Wulfmeier, Martin A. Riedmiller, Arthur Allshire, Qiang Wang, Robert McCarthy, Hangyeol Kim, Jongchan Baek, Wookyong Kwon, Shanliang Qian, Yasunori Toshimitsu, Mike Yan Michelis, Amirhossein Kazemipour, Arman Raayatsanati, Hehui Zheng, Barnabas Gavin Cangan, Bernhard Schölkopf, Georg Martius
2023	J	`jnl`	Replay across Experiments: A Natural Extension of Off-Policy RL. CoRR Dhruva Tirumala, Thomas Lampe, José Enrique Chen, Tuomas Haarnoja, Sandy H. Huang, Guy Lever, Ben Moran, Tim Hertweck, Leonard Hasenclever, Martin A. Riedmiller, Nicolas Heess, Markus Wulfmeier
2023	J	`jnl`	RoboCat: A Self-Improving Foundation Agent for Robotic Manipulation. CoRR Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Devin, Alex X. Lee, Maria Bauzá, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, Antoine Laurens, Claudio Fantacci, Valentin Dalibard, Martina Zambelli, Murilo F. Martins, Rugile Pevceviciute, Michiel Blokzijl, Misha Denil, Nathan Batchelor, Thomas Lampe, Emilio Parisotto, Konrad Zolna, Scott E. Reed, Sergio Gómez Colmenarejo, Jon Scholz, Abbas Abdolmaleki, Oliver Groth, Jean-Baptiste Regli, Oleg Sushkov, Thomas Rothörl, José Enrique Chen, Yusuf Aytar, Dave Barker, Joy Ortiz, Martin A. Riedmiller, Jost Tobias Springenberg, Raia Hadsell, Francesco Nori, Nicolas Heess
2023	J	`jnl`	SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration. Trans. Mach. Learn. Res. Giulia Vezzani, Dhruva Tirumala, Markus Wulfmeier, Dushyant Rao, Abbas Abdolmaleki, Ben Moran, Tuomas Haarnoja, Jan Humplik, Roland Hafner, Michael Neunert, Claudio Fantacci, Tim Hertweck, Thomas Lampe, Fereshteh Sadeghi, Nicolas Heess, Martin A. Riedmiller
2023	A*	`conf`	Solving Continuous Control via Q-learning. ICLR Tim Seyde, Peter Werner, Wilko Schwarting, Igor Gilitschenski, Martin A. Riedmiller, Daniela Rus, Markus Wulfmeier
2023	J	`jnl`	Towards A Unified Agent with Foundation Models. CoRR Norman Di Palo, Arunkumar Byravan, Leonard Hasenclever, Markus Wulfmeier, Nicolas Heess, Martin A. Riedmiller
2023	J	`jnl`	Towards practical reinforcement learning for tokamak magnetic control. CoRR Brendan D. Tracey, Andrea Michi, Yuri Chervonyi, Ian Davies, Cosmin Paduraru, Nevena Lazic, Federico Felici, Timo Ewalds, Craig Donner, Cristian Galperti, Jonas Buchli, Michael Neunert, Andrea Huber, Jonathan Evens, Paula Kurylowicz, Daniel J. Mankowitz, Martin A. Riedmiller, TCV Team
2022	A*	`conf`	Evaluating Model-Based Planning and Planner Amortization for Continuous Control. ICLR Arunkumar Byravan, Leonard Hasenclever, Piotr Trochim, Mehdi Mirza, Alessandro Davide Ialongo, Yuval Tassa, Jost Tobias Springenberg, Abbas Abdolmaleki, Nicolas Heess, Josh Merel, Martin A. Riedmiller
2022	—	`conf`	MO2: Model-Based Offline Options. CoLLAs Sasha Salter, Markus Wulfmeier, Dhruva Tirumala, Nicolas Heess, Martin A. Riedmiller, Raia Hadsell, Dushyant Rao
2022	J	`jnl`	MO2: Model-Based Offline Options. CoRR Sasha Salter, Markus Wulfmeier, Dhruva Tirumala, Nicolas Heess, Martin A. Riedmiller, Raia Hadsell, Dushyant Rao
2022	J	`jnl`	Magnetic control of tokamak plasmas through deep reinforcement learning. Nat. Jonas Degrave, Federico Felici, Jonas Buchli, Michael Neunert, Brendan D. Tracey, Francesco Carpanese, Timo Ewalds, Roland Hafner, Abbas Abdolmaleki, Diego de Las Casas, Craig Donner, Leslie Fritz, Cristian Galperti, Andrea Huber, James Keeling, Maria Tsimpoukelli, Jackie Kay, Antoine Merle, Jean-Marc Moret, Seb Noury, Federico Pesamosca, David Pfau, Olivier Sauter, Cristian Sommariva, Stefano Coda, Basil Duval, Ambrogio Fasoli, Pushmeet Kohli, Koray Kavukcuoglu, Demis Hassabis, Martin A. Riedmiller
2022	J	`jnl`	Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach. CoRR Bobak Shahriari, Abbas Abdolmaleki, Arunkumar Byravan, Abe Friesen, Siqi Liu, Jost Tobias Springenberg, Nicolas Heess, Matt Hoffman, Martin A. Riedmiller
2022	J	`jnl`	SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration. CoRR Giulia Vezzani, Dhruva Tirumala, Markus Wulfmeier, Dushyant Rao, Abbas Abdolmaleki, Ben Moran, Tuomas Haarnoja, Jan Humplik, Roland Hafner, Michael Neunert, Claudio Fantacci, Tim Hertweck, Thomas Lampe, Fereshteh Sadeghi, Nicolas Heess, Martin A. Riedmiller
2022	J	`jnl`	Solving Continuous Control via Q-learning. CoRR Tim Seyde, Peter Werner, Wilko Schwarting, Igor Gilitschenski, Martin A. Riedmiller, Daniela Rus, Markus Wulfmeier
2022	J	`jnl`	The Challenges of Exploration for Offline Reinforcement Learning. CoRR Nathan Lambert, Markus Wulfmeier, William F. Whitney, Arunkumar Byravan, Michael Bloesch, Vibhavari Dasagi, Tim Hertweck, Martin A. Riedmiller
2021	Misc	`conf`	A Constrained Multi-Objective Reinforcement Learning Framework. CoRL Sandy H. Huang, Abbas Abdolmaleki, Giulia Vezzani, Philemon Brakel, Daniel J. Mankowitz, Michael Neunert, Steven Bohez, Yuval Tassa, Nicolas Heess, Martin A. Riedmiller, Raia Hadsell
2021	Misc	`conf`	Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes. CoRL Alex X. Lee, Coline Manon Devin, Yuxiang Zhou, Thomas Lampe, Konstantinos Bousmalis, Jost Tobias Springenberg, Arunkumar Byravan, Abbas Abdolmaleki, Nimrod Gileadi, David Khosid, Claudio Fantacci, José Enrique Chen, Akhil Raju, Rae Jeong, Michael Neunert, Antoine Laurens, Stefano Saliceti, Federico Casarini, Martin A. Riedmiller, Raia Hadsell, Francesco Nori
2021	J	`jnl`	Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes. CoRR Alex X. Lee, Coline Devin, Yuxiang Zhou, Thomas Lampe, Konstantinos Bousmalis, Jost Tobias Springenberg, Arunkumar Byravan, Abbas Abdolmaleki, Nimrod Gileadi, David Khosid, Claudio Fantacci, José Enrique Chen, Akhil Raju, Rae Jeong, Michael Neunert, Antoine Laurens, Stefano Saliceti, Federico Casarini, Martin A. Riedmiller, Raia Hadsell, Francesco Nori
2021	Misc	`conf`	Collect & Infer - a fresh look at data-efficient Reinforcement Learning. CoRL Martin A. Riedmiller, Jost Tobias Springenberg, Roland Hafner, Nicolas Heess
2021	J	`jnl`	Collect & Infer - a fresh look at data-efficient Reinforcement Learning. CoRR Martin A. Riedmiller, Jost Tobias Springenberg, Roland Hafner, Nicolas Heess
2021	A*	`conf`	Data-efficient Hindsight Off-policy Option Learning. ICML Markus Wulfmeier, Dushyant Rao, Roland Hafner, Thomas Lampe, Abbas Abdolmaleki, Tim Hertweck, Michael Neunert, Dhruva Tirumala, Noah Y. Siegel, Nicolas Heess, Martin A. Riedmiller
2021	J	`jnl`	Evaluating model-based planning and planner amortization for continuous control. CoRR Arunkumar Byravan, Leonard Hasenclever, Piotr Trochim, Mehdi Mirza, Alessandro Davide Ialongo, Yuval Tassa, Jost Tobias Springenberg, Abbas Abdolmaleki, Nicolas Heess, Josh Merel, Martin A. Riedmiller
2021	A*	`conf`	Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies. NeurIPS Tim Seyde, Igor Gilitschenski, Wilko Schwarting, Bartolomeo Stellato, Martin A. Riedmiller, Markus Wulfmeier, Daniela Rus
2021	J	`jnl`	Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies. CoRR Tim Seyde, Igor Gilitschenski, Wilko Schwarting, Bartolomeo Stellato, Martin A. Riedmiller, Markus Wulfmeier, Daniela Rus
2021	J	`jnl`	Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration. CoRR Oliver Groth, Markus Wulfmeier, Giulia Vezzani, Vibhavari Dasagi, Tim Hertweck, Roland Hafner, Nicolas Heess, Martin A. Riedmiller
2021	J	`jnl`	On Multi-objective Policy Optimization as a Tool for Reinforcement Learning. CoRR Abbas Abdolmaleki, Sandy H. Huang, Giulia Vezzani, Bobak Shahriari, Jost Tobias Springenberg, Shruti Mishra, Dhruva TB, Arunkumar Byravan, Konstantinos Bousmalis, András György, Csaba Szepesvári, Raia Hadsell, Nicolas Heess, Martin A. Riedmiller
2021	—	`conf`	Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World. NeurIPS (Competition and Demos) Nico Gürtler, Felix Widmaier, Cansu Sancaktar, Sebastian Blaes, Pavel Kolev, Stefan Bauer, Manuel Wüthrich, Markus Wulfmeier, Martin A. Riedmiller, Arthur Allshire, Qiang Wang, Robert McCarthy, Hangyeol Kim, Jongchan Baek, Wookyong Kwon, Shanliang Qian, Yasunori Toshimitsu, Mike Yan Michelis, Amirhossein Kazemipour, Arman Raayatsanati, Hehui Zheng, Barnabas Gavin Cangan, Bernhard Schölkopf, Georg Martius
2021	A*	`conf`	Representation Matters: Improving Perception and Exploration for Robotics. ICRA Markus Wulfmeier, Arunkumar Byravan, Tim Hertweck, Irina Higgins, Ankush Gupta, Tejas Kulkarni, Malcolm Reynolds, Denis Teplyashin, Roland Hafner, Thomas Lampe, Martin A. Riedmiller
2021	J	`jnl`	Rethinking Exploration for Sample-Efficient Policy Learning. CoRR William F. Whitney, Michael Bloesch, Jost Tobias Springenberg, Abbas Abdolmaleki, Martin A. Riedmiller
2021	Misc	`conf`	Towards Real Robot Learning in the Wild: A Case Study in Bipedal Locomotion. CoRL Michael Bloesch, Jan Humplik, Viorica Patraucean, Roland Hafner, Tuomas Haarnoja, Arunkumar Byravan, Noah Yamamoto Siegel, Saran Tunyasuvunakool, Federico Casarini, Nathan Batchelor, Francesco Romano, Stefano Saliceti, Martin A. Riedmiller, S. M. Ali Eslami, Nicolas Heess
2020	J	`jnl`	"What, not how": Solving an under-actuated insertion task from scratch. CoRR Giulia Vezzani, Michael Neunert, Markus Wulfmeier, Rae Jeong, Thomas Lampe, Noah Y. Siegel, Roland Hafner, Abbas Abdolmaleki, Martin A. Riedmiller, Francesco Nori
2020	J	`jnl`	A Distributional View on Multi-Objective Policy Optimization. CoRR Abbas Abdolmaleki, Sandy H. Huang, Leonard Hasenclever, Michael Neunert, H. Francis Song, Martina Zambelli, Murilo F. Martins, Nicolas Heess, Raia Hadsell, Martin A. Riedmiller
2020	A*	`conf`	A distributional view on multi-objective policy optimization. ICML Abbas Abdolmaleki, Sandy H. Huang, Leonard Hasenclever, Michael Neunert, H. Francis Song, Martina Zambelli, Murilo F. Martins, Nicolas Heess, Raia Hadsell, Martin A. Riedmiller
2020	—	`conf`	Compositional Transfer in Hierarchical Reinforcement Learning. Robotics: Science and Systems Markus Wulfmeier, Abbas Abdolmaleki, Roland Hafner, Jost Tobias Springenberg, Michael Neunert, Noah Y. Siegel, Tim Hertweck, Thomas Lampe, Nicolas Heess, Martin A. Riedmiller
2020	J	`jnl`	Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics. CoRR Michael Neunert, Abbas Abdolmaleki, Markus Wulfmeier, Thomas Lampe, Jost Tobias Springenberg, Roland Hafner, Francesco Romano, Jonas Buchli, Nicolas Heess, Martin A. Riedmiller
2020	J	`jnl`	Data-efficient Hindsight Off-policy Option Learning. CoRR Markus Wulfmeier, Dushyant Rao, Roland Hafner, Thomas Lampe, Abbas Abdolmaleki, Tim Hertweck, Michael Neunert, Dhruva Tirumala, Noah Y. Siegel, Nicolas Heess, Martin A. Riedmiller
2020	A*	`conf`	Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning. ICLR Noah Y. Siegel, Jost Tobias Springenberg, Felix Berkenkamp, Abbas Abdolmaleki, Michael Neunert, Thomas Lampe, Roland Hafner, Nicolas Heess, Martin A. Riedmiller
2020	J	`jnl`	Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning. CoRR Noah Y. Siegel, Jost Tobias Springenberg, Felix Berkenkamp, Abbas Abdolmaleki, Michael Neunert, Thomas Lampe, Roland Hafner, Nicolas Heess, Martin A. Riedmiller
2020	J	`jnl`	Local Search for Policy Iteration in Continuous Control. CoRR Jost Tobias Springenberg, Nicolas Heess, Daniel J. Mankowitz, Josh Merel, Arunkumar Byravan, Abbas Abdolmaleki, Jackie Kay, Jonas Degrave, Julian Schrittwieser, Yuval Tassa, Jonas Buchli, Dan Belov, Martin A. Riedmiller
2020	J	`jnl`	Representation Matters: Improving Perception and Exploration for Robotics. CoRR Markus Wulfmeier, Arunkumar Byravan, Tim Hertweck, Irina Higgins, Ankush Gupta, Tejas Kulkarni, Malcolm Reynolds, Denis Teplyashin, Roland Hafner, Thomas Lampe, Martin A. Riedmiller
2020	J	`jnl`	Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification. CoRR Daniel J. Mankowitz, Dan A. Calian, Rae Jeong, Cosmin Paduraru, Nicolas Heess, Sumanth Dathathri, Martin A. Riedmiller, Timothy A. Mann
2020	A*	`conf`	Robust Reinforcement Learning for Continuous Control with Model Misspecification. ICLR Daniel J. Mankowitz, Nir Levine, Rae Jeong, Abbas Abdolmaleki, Jost Tobias Springenberg, Yuanyuan Shi, Jackie Kay, Todd Hester, Timothy A. Mann, Martin A. Riedmiller
2020	J	`jnl`	Simple Sensor Intentions for Exploration. CoRR Tim Hertweck, Martin A. Riedmiller, Michael Bloesch, Jost Tobias Springenberg, Noah Y. Siegel, Markus Wulfmeier, Roland Hafner, Nicolas Heess
2020	Misc	`conf`	Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion. CoRL Roland Hafner, Tim Hertweck, Philipp Klöppner, Michael Bloesch, Michael Neunert, Markus Wulfmeier, Saran Tunyasuvunakool, Nicolas Heess, Martin A. Riedmiller
2020	J	`jnl`	Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion. CoRR Roland Hafner, Tim Hertweck, Philipp Klöppner, Michael Bloesch, Michael Neunert, Markus Wulfmeier, Saran Tunyasuvunakool, Nicolas Heess, Martin A. Riedmiller
2020	A*	`conf`	V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control. ICLR H. Francis Song, Abbas Abdolmaleki, Jost Tobias Springenberg, Aidan Clark, Hubert Soyer, Jack W. Rae, Seb Noury, Arun Ahuja, Siqi Liu, Dhruva Tirumala, Nicolas Heess, Dan Belov, Martin A. Riedmiller, Matthew M. Botvinick
2019	J	`jnl`	Adaptive long-term control of biological neural networks with Deep Reinforcement Learning. Neurocomputing Jan M. Wülfing, Sreedhar S. Kumar, Joschka Boedecker, Martin A. Riedmiller, Ulrich Egert
2019	Misc	`conf`	Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics. CoRL Michael Neunert, Abbas Abdolmaleki, Markus Wulfmeier, Thomas Lampe, Jost Tobias Springenberg, Roland Hafner, Francesco Romano, Jonas Buchli, Nicolas Heess, Martin A. Riedmiller
2019	Misc	`conf`	Imagined Value Gradients: Model-Based Policy Optimization with Tranferable Latent Dynamics Models. CoRL Arunkumar Byravan, Jost Tobias Springenberg, Abbas Abdolmaleki, Roland Hafner, Michael Neunert, Thomas Lampe, Noah Y. Siegel, Nicolas Heess, Martin A. Riedmiller
2019	J	`jnl`	Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models. CoRR Arunkumar Byravan, Jost Tobias Springenberg, Abbas Abdolmaleki, Roland Hafner, Michael Neunert, Thomas Lampe, Noah Y. Siegel, Nicolas Heess, Martin A. Riedmiller
2019	J	`jnl`	Quinoa: a Q-function You Infer Normalized Over Actions. CoRR Jonas Degrave, Abbas Abdolmaleki, Jost Tobias Springenberg, Nicolas Heess, Martin A. Riedmiller
2019	J	`jnl`	Regularized Hierarchical Policies for Compositional Transfer in Robotics. CoRR Markus Wulfmeier, Abbas Abdolmaleki, Roland Hafner, Jost Tobias Springenberg, Michael Neunert, Tim Hertweck, Thomas Lampe, Noah Y. Siegel, Nicolas Heess, Martin A. Riedmiller
2019	J	`jnl`	Robust Reinforcement Learning for Continuous Control with Model Misspecification. CoRR Daniel J. Mankowitz, Nir Levine, Rae Jeong, Abbas Abdolmaleki, Jost Tobias Springenberg, Timothy A. Mann, Todd Hester, Martin A. Riedmiller
2019	J	`jnl`	Self-supervised Learning of Image Embedding for Continuous Control. CoRR Carlos Florensa, Jonas Degrave, Nicolas Heess, Jost Tobias Springenberg, Martin A. Riedmiller
2019	—	`conf`	Simultaneously Learning Vision and Feature-Based Control Policies for Real-World Ball-In-A-Cup. Robotics: Science and Systems Devin Schwab, Jost Tobias Springenberg, Murilo Fernandes Martins, Michael Neunert, Thomas Lampe, Abbas Abdolmaleki, Tim Hertweck, Roland Hafner, Francesco Nori, Martin A. Riedmiller
2019	J	`jnl`	Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup. CoRR Devin Schwab, Jost Tobias Springenberg, Murilo F. Martins, Thomas Lampe, Michael Neunert, Abbas Abdolmaleki, Tim Hertweck, Roland Hafner, Francesco Nori, Martin A. Riedmiller
2019	J	`jnl`	V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control. CoRR H. Francis Song, Abbas Abdolmaleki, Jost Tobias Springenberg, Aidan Clark, Hubert Soyer, Jack W. Rae, Seb Noury, Arun Ahuja, Siqi Liu, Dhruva Tirumala, Nicolas Heess, Dan Belov, Martin A. Riedmiller, Matthew M. Botvinick
2018	B	`conf`	Controlling biological neural networks with deep reinforcement learning. ESANN Jan Wülfing, Sreedhar S. Kumar, Joschka Boedecker, Martin A. Riedmiller, Ulrich Egert
2018	J	`jnl`	DeepMind Control Suite. CoRR Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, Timothy P. Lillicrap, Martin A. Riedmiller
2018	A*	`conf`	Graph Networks as Learnable Physics Engines for Inference and Control. ICML Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin A. Riedmiller, Raia Hadsell, Peter W. Battaglia
2018	J	`jnl`	Graph networks as learnable physics engines for inference and control. CoRR Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin A. Riedmiller, Raia Hadsell, Peter W. Battaglia
2018	—	`conf`	Learning an Embedding Space for Transferable Robot Skills. ICLR (Poster) Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, Martin A. Riedmiller
2018	J	`jnl`	Learning by Playing - Solving Sparse Reward Tasks from Scratch. CoRR Martin A. Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Van de Wiele, Volodymyr Mnih, Nicolas Heess, Jost Tobias Springenberg
2018	A*	`conf`	Learning by Playing Solving Sparse Reward Tasks from Scratch. ICML Martin A. Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Van de Wiele, Vlad Mnih, Nicolas Heess, Jost Tobias Springenberg
2018	—	`conf`	Maximum a Posteriori Policy Optimisation. ICLR (Poster) Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Rémi Munos, Nicolas Heess, Martin A. Riedmiller
2018	J	`jnl`	Maximum a Posteriori Policy Optimisation. CoRR Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Rémi Munos, Nicolas Heess, Martin A. Riedmiller
2018	J	`jnl`	Relative Entropy Regularized Policy Iteration. CoRR Abbas Abdolmaleki, Jost Tobias Springenberg, Jonas Degrave, Steven Bohez, Yuval Tassa, Dan Belov, Nicolas Heess, Martin A. Riedmiller
2017	J	`jnl`	Data-efficient Deep Reinforcement Learning for Dexterous Manipulation. CoRR Ivaylo Popov, Nicolas Heess, Timothy P. Lillicrap, Roland Hafner, Gabriel Barth-Maron, Matej Vecerík, Thomas Lampe, Yuval Tassa, Tom Erez, Martin A. Riedmiller
2017	J	`jnl`	Emergence of Locomotion Behaviours in Rich Environments. CoRR Nicolas Heess, Dhruva TB, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, S. M. Ali Eslami, Martin A. Riedmiller, David Silver
2017	J	`jnl`	Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards. CoRR Matej Vecerík, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, Martin A. Riedmiller
2017	J	`jnl`	PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations. CoRR Rico Jonschkowski, Roland Hafner, Jonathan Scholz, Martin A. Riedmiller
2016	J	`jnl`	Autonomous Optimization of Targeted Stimulation of Neuronal Networks. PLoS Comput. Biol. Sreedhar S. Kumar, Jan Wülfing, Samora Okujeni, Joschka Boedecker, Martin A. Riedmiller, Ulrich Egert
2016	J	`jnl`	Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks. IEEE Trans. Pattern Anal. Mach. Intell. Alexey Dosovitskiy, Philipp Fischer, Jost Tobias Springenberg, Martin A. Riedmiller, Thomas Brox
2016	J	`jnl`	Learning and Transfer of Modulated Locomotor Controllers. CoRR Nicolas Heess, Gregory Wayne, Yuval Tassa, Timothy P. Lillicrap, Martin A. Riedmiller, David Silver
2015	J	`jnl`	Autonomous Learning of State Representations for Control: An Emerging Field Aims to Autonomously Learn State Representations for Reinforcement Learning Agents from Their Real-World Sensor Observations. Künstliche Intell. Wendelin Böhmer, Jost Tobias Springenberg, Joschka Boedecker, Martin A. Riedmiller, Klaus Obermayer
2015	—	`conf`	Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images. NIPS Manuel Watter, Jost Tobias Springenberg, Joschka Boedecker, Martin A. Riedmiller
2015	J	`jnl`	Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images. CoRR Manuel Watter, Jost Tobias Springenberg, Joschka Boedecker, Martin A. Riedmiller
2015	J	`jnl`	Human-level control through deep reinforcement learning. Nat. Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis
2015	J	`jnl`	Multimodal Deep Learning for Robust RGB-D Object Recognition. CoRR Andreas Eitel, Jost Tobias Springenberg, Luciano Spinello, Martin A. Riedmiller, Wolfram Burgard
2015	A	`conf`	Multimodal deep learning for robust RGB-D object recognition. IROS Andreas Eitel, Jost Tobias Springenberg, Luciano Spinello, Martin A. Riedmiller, Wolfram Burgard
2015	—	`conf`	Striving for Simplicity: The All Convolutional Net. ICLR (Workshop) Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, Martin A. Riedmiller
2014	A	`conf`	A brain-computer interface for high-level remote control of an autonomous, reinforcement-learning-based robotic system for reaching and grasping. IUI Thomas Lampe, Lukas Dominique Josef Fiederer, Martin Voelker, Alexander Knorr, Martin A. Riedmiller, Tonio Ball
2014	B	`conf`	Approximate model-assisted Neural Fitted Q-Iteration. IJCNN Thomas Lampe, Martin A. Riedmiller
2014	—	`conf`	Approximate real-time optimal control based on sparse Gaussian process models. ADPRL Joschka Boedecker, Jost Tobias Springenberg, Jan Wülfing, Martin A. Riedmiller
2014	A*	`conf`	Deterministic Policy Gradient Algorithms. ICML David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, Martin A. Riedmiller
2014	—	`conf`	Discriminative Unsupervised Feature Learning with Convolutional Neural Networks. NIPS Alexey Dosovitskiy, Jost Tobias Springenberg, Martin A. Riedmiller, Thomas Brox
2014	J	`jnl`	Discriminative Unsupervised Feature Learning with Convolutional Neural Networks. CoRR Alexey Dosovitskiy, Jost Tobias Springenberg, Martin A. Riedmiller, Thomas Brox
2014	—	`conf`	Improving Deep Neural Networks with Probabilistic Maxout Units. ICLR (Workshop Poster) Jost Tobias Springenberg, Martin A. Riedmiller
2013	B	`conf`	Acquiring visual servoing reaching and grasping skills using neural reinforcement learning. IJCNN Thomas Lampe, Martin A. Riedmiller
2013	—	`conf`	Electricity Demand Forecasting using Gaussian Processes. AAAI Workshop: Trading Agent Design and Analysis Manuel Blum, Martin A. Riedmiller
2013	—	`conf`	Improvement of a Web Browser Game Through the Knowledge Extracted from Player Behavior. KICSS João Alves, José Neves, Sascha Lange, Martin A. Riedmiller
2013	—	`conf`	Learning machines that perceive, act and communicate. MLIS@IJCAI Martin A. Riedmiller
2013	B	`conf`	Optimization of Gaussian process hyperparameters using Rprop. ESANN Manuel Blum, Martin A. Riedmiller
2013	J	`jnl`	Playing Atari with Deep Reinforcement Learning. CoRR Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin A. Riedmiller
2012	—	`ch.`	10 Steps and Some Tricks to Set up Neural Reinforcement Controllers. Neural Networks: Tricks of the Trade (2nd ed.) Martin A. Riedmiller
2012	A*	`conf`	A learned feature descriptor for object recognition in RGB-D data. ICRA Manuel Blum, Jost Tobias Springenberg, Jan Wülfing, Martin A. Riedmiller
2012	B	`conf`	Autonomous reinforcement learning on raw visual input data in a real world application. IJCNN Sascha Lange, Martin A. Riedmiller, Arne Voigtländer
2012	—	`ch.`	Batch Reinforcement Learning. Reinforcement Learning Sascha Lange, Thomas Gabel, Martin A. Riedmiller
2012	—	`conf`	Learn to Swing Up and Balance a Real Pole Based on Raw Visual Input Data. ICONIP (5) Jan Mattner, Sascha Lange, Martin A. Riedmiller
2012	—	`conf`	Learning Temporal Coherent Features through Life-Time Sparsity. ICONIP (1) Jost Tobias Springenberg, Martin A. Riedmiller
2012	B	`conf`	Taming the reservoir: Feedforward training for recurrent neural networks. IJCNN Oliver Obst, Martin A. Riedmiller
2012	—	`conf`	Unsupervised Learning of Local Features for Music Classification. ISMIR Jan Wülfing, Martin A. Riedmiller
2011	—	`conf`	Enhancing the episodic natural actor-critic algorithm by a regularisation term to stabilize learning of control structures. ADPRL Andreas Witsch, Roland Reichle, Kurt Geihs, Sascha Lange, Martin A. Riedmiller
2011	—	`conf`	Improved neural fitted Q iteration applied to a novel computer gaming and learning benchmark. ADPRL Thomas Gabel, Christian Lutz, Martin A. Riedmiller
2011	J	`jnl`	Reinforcement learning in feedback control - Challenges and benchmarks from technical process control. Mach. Learn. Roland Hafner, Martin A. Riedmiller
2010	J	`jnl`	Cognitive concepts in autonomous soccer playing robots. Cogn. Syst. Res. Martin Lauer, Roland Hafner, Sascha Lange, Martin A. Riedmiller
2010	B	`conf`	Deep auto-encoder neural networks in reinforcement learning. IJCNN Sascha Lange, Martin A. Riedmiller
2010	B	`conf`	Deep learning of visual control policies. ESANN Sascha Lange, Martin A. Riedmiller
2010	C	`conf`	On Progress in RoboCup: The Simulation League Showcase. RoboCup Thomas Gabel, Martin A. Riedmiller
2009	—	`conf`	09371 Abstracts Collection - Algorithmic Methods for Distributed Cooperative Systems. Algorithmic Methods for Distributed Cooperative Systems Sándor P. Fekete, Stefan Fischer, Martin A. Riedmiller, Subhash Suri
2009	—	`ed.`	Algorithmic Methods for Distributed Cooperative Systems, 06.09. - 11.09.2009 Algorithmic Methods for Distributed Cooperative Systems Sándor P. Fekete, Stefan Fischer, Martin A. Riedmiller, Subhash Suri
2009	J	`jnl`	Computational object recognition: a biologically motivated approach. Biol. Cybern. Tim C. Kietzmann, Sascha Lange, Martin A. Riedmiller
2009	J	`jnl`	Efficient Identification of State in Reinforcement Learning. Künstliche Intell. Stephan Timmer, Martin A. Riedmiller
2009	J	`jnl`	Reinforcement learning for robot soccer. Auton. Robots Martin A. Riedmiller, Thomas Gabel, Roland Hafner, Sascha Lange
2009	C	`conf`	The Neuro Slot Car Racer: Reinforcement Learning in a Real World Setting. ICMLA Tim C. Kietzmann, Martin A. Riedmiller
2008	C	`conf`	A Case Study on Improving Defense Behavior in Soccer Simulation 2D: The NeuroHassle Approach. RoboCup Thomas Gabel, Martin A. Riedmiller, Florian Trost
2008	—	`conf`	Evaluation of Batch-Mode Reinforcement Learning Methods for Solving DEC-MDPs with Changing Action Sets. EWRL Thomas Gabel, Martin A. Riedmiller
2008	—	`conf`	Increasing Precision of Credible Case-Based Inference. ECCBR Thomas Gabel, Martin A. Riedmiller
2008	J	`jnl`	Incremental GRLVQ: Learning relevant features for 3D object recognition. Neurocomputing Tim C. Kietzmann, Sascha Lange, Martin A. Riedmiller
2008	—	`conf`	Joint Equilibrium Policy Search for Multi-Agent Scheduling Problems. MATES Thomas Gabel, Martin A. Riedmiller
2008	A*	`conf`	Learning to dribble on a real robot by success and failure. ICRA Martin A. Riedmiller, Roland Hafner, Sascha Lange, Martin Lauer
2008	—	`conf`	Reinforcement learning for DEC-MDPs with changing action sets and partially ordered dependencies. AAMAS (3) Thomas Gabel, Martin A. Riedmiller
2007	C	`conf`	An Analysis of Case-Based Value Function Approximation by Approximating State Transition Graphs. ICCBR Thomas Gabel, Martin A. Riedmiller
2007	—	`conf`	Learning to Drive a Real Car in 20 Minutes. FBIT Martin A. Riedmiller, Michael Montemerlo, Hendrik Dahlkamp
2007	Misc	`conf`	Making a Robot Learn to Play Soccer Using Reward and Punishment. KI Heiko Müller, Martin Lauer, Roland Hafner, Sascha Lange, Artur Merke, Martin A. Riedmiller
2007	A*	`conf`	Neural Reinforcement Learning Controllers for a Real Robot Application. ICRA Roland Hafner, Martin A. Riedmiller
2007	—	`conf`	On Experiences in a Complex and Competitive Gaming Domain: Reinforcement Learning Meets RoboCup. CIG Martin A. Riedmiller, Thomas Gabel
2007	—	`conf`	Real-time 3D Ball Recognition using Perspective and Catadioptric Cameras. EMCR Arne Voigtländer, Sascha Lange, Martin Lauer, Martin A. Riedmiller
2007	B	`conf`	Reinforcement learning in a nutshell. ESANN Verena Heidrich-Meisner, Martin Lauer, Christian Igel, Martin A. Riedmiller
2007	—	`conf`	Safe Q-Learning on Complete History Spaces. ECML Stephan Timmer, Martin A. Riedmiller
2007	—	`conf`	Scaling Adaptive Agent-Based Reactive Job-Shop Scheduling to Large-Scale Problems. CISched Thomas Gabel, Martin A. Riedmiller
2006	—	`conf`	06251 Abstracts Collection - Multi-Robot Systems: Perception, Behaviors, Learning, and Action. Multi-Robot Systems: Perception, Behaviors, Learning, and Action Hans-Dieter Burkhard, Martin A. Riedmiller, Uwe Schwiegelshohn, Manuela M. Veloso
2006	C	`conf`	Appearance-Based Robot Discrimination Using Eigenimages. RoboCup Sascha Lange, Martin A. Riedmiller
2006	J	`jnl`	Die Brainstormers: Entwurfsprinzipien lernfähiger autonomer Roboter. Inform. Spektrum Martin A. Riedmiller, Thomas Gabel, Roland Hafner, Sascha Lange, Martin Lauer
2006	J	`jnl`	Learning a Partial Behavior for a Competitive Robotic Soccer Agent. Künstliche Intell. Thomas Gabel, Martin A. Riedmiller
2006	—	`ed.`	Multi-Robot Systems: Perception, Behaviors, Learning, and Action, 19.06. - 23.06.2006 Multi-Robot Systems: Perception, Behaviors, Learning, and Action Hans-Dieter Burkhard, Martin A. Riedmiller, Uwe Schwiegelshohn, Manuela M. Veloso
2006	—	`conf`	Multi-agent Case-Based Reasoning for Cooperative Reinforcement Learners. ECCBR Thomas Gabel, Martin A. Riedmiller
2006	B	`conf`	Reducing policy degradation in neuro-dynamic programming. ESANN Thomas Gabel, Martin A. Riedmiller
2005	C	`conf`	CBR for State Value Function Approximation in Reinforcement Learning. ICCBR Thomas Gabel, Martin A. Riedmiller
2005	C	`conf`	Calculating the Perfect Match: An Efficient and Accurate Approach for Robot Self-localization. RoboCup Martin Lauer, Sascha Lange, Martin A. Riedmiller
2005	B	`conf`	Comparing different methods to speed up reinforcement learning in a complex domain. SMC Martin A. Riedmiller, Daniel Withopf
2005	J	`jnl`	Effective Methods for Reinforcement Learning in Large Multi-Agent Domains. it Inf. Technol. Martin A. Riedmiller, Daniel Withopf
2005	B	`conf`	Learning policies for abstract state spaces. SMC Stephan Timmer, Martin A. Riedmiller
2005	Misc	`conf`	Modeling Moving Objects in a Dynamically Changing Robot Application. KI Martin Lauer, Sascha Lange, Martin A. Riedmiller
2005	—	`conf`	Neural Fitted Q Iteration - First Experiences with a Data Efficient Neural Reinforcement Learning Method. ECML Martin A. Riedmiller
2005	B	`conf`	Neural reinforcement learning to swing-up and balance a real pole. SMC Martin A. Riedmiller
2005	—	`conf`	Reinforcement Learning Using a Grid Based Function Approximator. Biomimetic Neural Learning for Intelligent Robots Alexander Sung, Artur Merke, Martin A. Riedmiller
2005	C	`ed.`	RoboCup 2004: Robot Soccer World Cup VIII RoboCup Daniele Nardi, Martin A. Riedmiller, Claude Sammut, José Santos-Victor
2004	C	`conf`	Evolution of Computer Vision Subsystems in Robot Navigation and Image Classification Tasks. RoboCup Sascha Lange, Martin A. Riedmiller
2004	J	`jnl`	Fynesse: An architecture for integrating prior knowledge in autonomously learning agents. Soft Comput. Ralf Schoknecht, Martin Spott, Martin A. Riedmiller
2004	J	`jnl`	Invited talks. Künstliche Intell. Martin A. Riedmiller, François Fages, Malik Ghallab, Wolfgang Wahlster, Jörg H. Siekmann
2004	Misc	`conf`	Machine Learning for Autonomous Robots. KI Martin A. Riedmiller
2004	A	`conf`	Reinforcement Learning for Stochastic Cooperative Multi-Agent Systems. AAMAS Martin Lauer, Martin A. Riedmiller
2004	J	`jnl`	RoboCup-2003: New Scientific and Technical Advances. AI Mag. Enrico Pagello, Emanuele Menegatti, Ansgar Bredenfeld, Paulo Costa, Thomas Christaller, Adam Jacoff, Daniel Polani, Martin A. Riedmiller, Alessandro Saffiotti, Elizabeth Sklar, Takashi Tomoichi
2003	C	`conf`	Learning to Control at Multiple Time Scales. ICANN Ralf Schoknecht, Martin A. Riedmiller
2003	C	`conf`	Overview of RoboCup 2003 Competition and Conferences. RoboCup Enrico Pagello, Emanuele Menegatti, Ansgar Bredenfeld, Paulo Costa, Thomas Christaller, Adam Jacoff, Jeffrey Johnson, Martin A. Riedmiller, Alessandro Saffiotti, Takashi Tomoichi
2003	A	`conf`	Reinforcement learning on an omnidirectional mobile robot. IROS Roland Hafner, Martin A. Riedmiller
2003	J	`jnl`	Reinforcement learning on explicitly specified time scales. Neural Comput. Appl. Ralf Schoknecht, Martin A. Riedmiller
2003	C	`conf`	RoboCup: Yesterday, Today, and Tomorrow Workshop of the Executive Committee in Blaubeuren, October 2003. RoboCup Hans-Dieter Burkhard, Minoru Asada, Andrea Bonarini, Adam Jacoff, Daniele Nardi, Martin A. Riedmiller, Claude Sammut, Elizabeth Sklar, Manuela M. Veloso
2003	B	`conf`	The Smaller the Better: Comparison of Two Approaches for Sales Rate Prediction. IDA Martin Lauer, Martin A. Riedmiller, Thomas Ragg, Walter Baum, Michael Wigbers
2002	C	`conf`	Speeding-up Reinforcement Learning with Multi-step Actions. ICANN Ralf Schoknecht, Martin A. Riedmiller
2001	C	`conf`	Karlsruhe Brainstormers - A Reinforcement Learning Approach to Robotic Soccer. RoboCup Artur Merke, Martin A. Riedmiller
2000	A*	`conf`	An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems. ICML Martin Lauer, Martin A. Riedmiller
2000	C	`conf`	Karlsruhe Brainstormers - A Reinforcement Learning Approach to Robotic Soccer. RoboCup Martin A. Riedmiller, Artur Merke, David Meier, Andreas Hoffmann, Alex Sinner, Ortwin Thate, R. Ehrmann
2000	C	`conf`	Karlsruhe Brainstormers 2000 Team Description. RoboCup Martin A. Riedmiller, Artur Merke, David Meier, Andreas Hoffmann, Alex Sinner, Ortwin Thate
2000	B	`conf`	Learning Situation Dependent Success Rates of Actions in a RoboCup Scenario. PRICAI Sebastian Buck, Martin A. Riedmiller
2000	—	`conf`	Reinforcement Learning for Cooperating and Communicating Reactive Agents in Electrical Power Grids. Balancing Reactivity and Social Deliberation in Multi-Agent Systems Martin A. Riedmiller, Andrew W. Moore, Jeff G. Schneider
1999	A*	`conf`	A Neural Reinforcement Learning Approach to Learn Local Dispatching Policies in Production Scheduling. IJCAI Simone C. Riedmiller, Martin A. Riedmiller
1999	J	`jnl`	Concepts and Facilities of a Neural Reinforcement Learning Control Architecture for Technical Process Control. Neural Comput. Appl. Martin A. Riedmiller
1999	A*	`conf`	Distributed Value Functions. ICML Jeff G. Schneider, Weng-Keen Wong, Andrew W. Moore, Martin A. Riedmiller
1999	C	`conf`	Karlsruhe Brainstormers - Design Principles. RoboCup Martin A. Riedmiller, Sebastian Buck, Artur Merke, R. Ehrmann, Ortwin Thate, S. Dilger, Alex Sinner, Andreas Hoffmann, Lutz Frommberger
1998	J	`jnl`	A Neural Approach for the Control of Piezoelectric Micromanipulation Robots. J. Intell. Robotic Syst. Karoly Santa, Michael Mews, Martin A. Riedmiller
1997	—	`conf`	A new method for the analysis of neural reference model control. ICNN Michael Wigbers, Martin A. Riedmiller
1997	B	`conf`	Application of a self-learning controller with continuous control signals based on the DOE-approach. ESANN Martin A. Riedmiller
1997	—	`—`	Selbständig lernende neuronale Steuerungen. Martin A. Riedmiller
1996	—	`conf`	Application of sequential reinforcement learning to control dynamic systems. ICNN Martin A. Riedmiller
1996	—	`conf`	Fast Network Pruning and Feature Extraction by using the Unit-OBS Algorithm. NIPS Achim Stahlberger, Martin A. Riedmiller
1995	—	`conf`	Massively Parallel Training of Multi Layer Perceptrons With Irregular Topologies. ICANNGA D. Koll, Martin A. Riedmiller, Heinrich Braun
1995	—	`conf`	Self-learning neural control of a mobile robot. ICNN Barbara Janusz, Martin A. Riedmiller
1993	—	`conf`	A direct adaptive method for faster backpropagation learning: the RPROP algorithm. ICNN Martin A. Riedmiller, Heinrich Braun