Martin A. Riedmiller
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
74
Venues
19
Active years
1997–2025
Best venue rank
A*
Where they publish
Papers
74 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ICLR | Learning from negative feedback, or positive feedback or both. | 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 | ICRA | DemoStart: Demonstration-Led Auto-Curriculum Applied to Sim-to-Real with Multi-Fingered Robots. | Maria Bauz, 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 |
| 2024 | CoRL | Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning. | 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 | ICLR | Replay across Experiments: A Natural Extension of Off-Policy RL. | Dhruva Tirumala, Thomas Lampe, Jos Enrique Chen, Tuomas Haarnoja, Sandy H. Huang, Guy Lever, Ben Moran, Tim Hertweck, Leonard Hasenclever, Martin A. Riedmiller, Nicolas Heess, Markus Wulfmeier |
| 2024 | ICML | Offline Actor-Critic Reinforcement Learning Scales to Large Models. | 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 | ICRA | Mastering Stacking of Diverse Shapes with Large-Scale Iterative Reinforcement Learning on Real Robots. | 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 | ICLR | Solving Continuous Control via Q-learning. | Tim Seyde, Peter Werner, Wilko Schwarting, Igor Gilitschenski, Martin A. Riedmiller, Daniela Rus, Markus Wulfmeier |
| 2022 | ICLR | Evaluating Model-Based Planning and Planner Amortization for Continuous Control. | 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 | CoRL | Towards Real Robot Learning in the Wild: A Case Study in Bipedal Locomotion. | 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 |
| 2021 | CoRL | A Constrained Multi-Objective Reinforcement Learning Framework. | 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 | CoRL | Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes. | 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, Jos Enrique Chen, Akhil Raju, Rae Jeong, Michael Neunert, Antoine Laurens, Stefano Saliceti, Federico Casarini, Martin A. Riedmiller, Raia Hadsell, Francesco Nori |
| 2021 | CoRL | Collect & Infer - a fresh look at data-efficient Reinforcement Learning. | Martin A. Riedmiller, Jost Tobias Springenberg, Roland Hafner, Nicolas Heess |
| 2021 | ICML | Data-efficient Hindsight Off-policy Option Learning. | 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 | ICRA | Representation Matters: Improving Perception and Exploration for Robotics. | Markus Wulfmeier, Arunkumar Byravan, Tim Hertweck, Irina Higgins, Ankush Gupta, Tejas Kulkarni, Malcolm Reynolds, Denis Teplyashin, Roland Hafner, Thomas Lampe, Martin A. Riedmiller |
| 2020 | CoRL | Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion. | Roland Hafner, Tim Hertweck, Philipp Klppner, Michael Bloesch, Michael Neunert, Markus Wulfmeier, Saran Tunyasuvunakool, Nicolas Heess, Martin A. Riedmiller |
| 2020 | ICLR | Robust Reinforcement Learning for Continuous Control with Model Misspecification. | 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 | ICLR | Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning. | Noah Y. Siegel, Jost Tobias Springenberg, Felix Berkenkamp, Abbas Abdolmaleki, Michael Neunert, Thomas Lampe, Roland Hafner, Nicolas Heess, Martin A. Riedmiller |
| 2020 | ICLR | V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control. | 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 |
| 2020 | ICML | A distributional view on multi-objective policy optimization. | Abbas Abdolmaleki, Sandy H. Huang, Leonard Hasenclever, Michael Neunert, H. Francis Song, Martina Zambelli, Murilo F. Martins, Nicolas Heess, Raia Hadsell, Martin A. Riedmiller |
| 2019 | CoRL | Imagined Value Gradients: Model-Based Policy Optimization with Tranferable Latent Dynamics Models. | Arunkumar Byravan, Jost Tobias Springenberg, Abbas Abdolmaleki, Roland Hafner, Michael Neunert, Thomas Lampe, Noah Y. Siegel, Nicolas Heess, Martin A. Riedmiller |
| 2019 | CoRL | Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics. | Michael Neunert, Abbas Abdolmaleki, Markus Wulfmeier, Thomas Lampe, Jost Tobias Springenberg, Roland Hafner, Francesco Romano, Jonas Buchli, Nicolas Heess, Martin A. Riedmiller |
| 2018 | ESANN | Controlling biological neural networks with deep reinforcement learning. | Jan Wlfing, Sreedhar S. Kumar, Joschka Boedecker, Martin A. Riedmiller, Ulrich Egert |
| 2018 | ICLR | Maximum a Posteriori Policy Optimisation. | Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Rmi Munos, Nicolas Heess, Martin A. Riedmiller |
| 2018 | ICLR | Learning an Embedding Space for Transferable Robot Skills. | Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, Martin A. Riedmiller |
| 2018 | ICML | Learning by Playing Solving Sparse Reward Tasks from Scratch. | Martin A. Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Van de Wiele, Vlad Mnih, Nicolas Heess, Jost Tobias Springenberg |
| 2018 | ICML | Graph Networks as Learnable Physics Engines for Inference and Control. | Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin A. Riedmiller, Raia Hadsell, Peter W. Battaglia |
| 2015 | IROS | Multimodal deep learning for robust RGB-D object recognition. | Andreas Eitel, Jost Tobias Springenberg, Luciano Spinello, Martin A. Riedmiller, Wolfram Burgard |
| 2014 | ICML | Deterministic Policy Gradient Algorithms. | David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, Martin A. Riedmiller |
| 2014 | IJCNN | Approximate model-assisted Neural Fitted Q-Iteration. | Thomas Lampe, Martin A. Riedmiller |
| 2014 | IUI | A brain-computer interface for high-level remote control of an autonomous, reinforcement-learning-based robotic system for reaching and grasping. | Thomas Lampe, Lukas Dominique Josef Fiederer, Martin Voelker, Alexander Knorr, Martin A. Riedmiller, Tonio Ball |
| 2013 | AAAI | Electricity Demand Forecasting using Gaussian Processes. | Manuel Blum, Martin A. Riedmiller |
| 2013 | ESANN | Optimization of Gaussian process hyperparameters using Rprop. | Manuel Blum, Martin A. Riedmiller |
| 2013 | IJCAI | Learning machines that perceive, act and communicate. | Martin A. Riedmiller |
| 2013 | IJCNN | Acquiring visual servoing reaching and grasping skills using neural reinforcement learning. | Thomas Lampe, Martin A. Riedmiller |
| 2012 | ICONIP | Learn to Swing Up and Balance a Real Pole Based on Raw Visual Input Data. | Jan Mattner, Sascha Lange, Martin A. Riedmiller |
| 2012 | ICONIP | Learning Temporal Coherent Features through Life-Time Sparsity. | Jost Tobias Springenberg, Martin A. Riedmiller |
| 2012 | ICRA | A learned feature descriptor for object recognition in RGB-D data. | Manuel Blum, Jost Tobias Springenberg, Jan Wlfing, Martin A. Riedmiller |
| 2012 | IJCNN | Autonomous reinforcement learning on raw visual input data in a real world application. | Sascha Lange, Martin A. Riedmiller, Arne Voigtlnder |
| 2012 | IJCNN | Taming the reservoir: Feedforward training for recurrent neural networks. | Oliver Obst, Martin A. Riedmiller |
| 2010 | ESANN | Deep learning of visual control policies. | Sascha Lange, Martin A. Riedmiller |
| 2010 | IJCNN | Deep auto-encoder neural networks in reinforcement learning. | Sascha Lange, Martin A. Riedmiller |
| 2010 | RoboCup | On Progress in RoboCup: The Simulation League Showcase. | Thomas Gabel, Martin A. Riedmiller |
| 2009 | ICMLA | The Neuro Slot Car Racer: Reinforcement Learning in a Real World Setting. | Tim C. Kietzmann, Martin A. Riedmiller |
| 2008 | ICRA | Learning to dribble on a real robot by success and failure. | Martin A. Riedmiller, Roland Hafner, Sascha Lange, Martin Lauer |
| 2008 | RoboCup | A Case Study on Improving Defense Behavior in Soccer Simulation 2D: The NeuroHassle Approach. | Thomas Gabel, Martin A. Riedmiller, Florian Trost |
| 2007 | ESANN | Reinforcement learning in a nutshell. | Verena Heidrich-Meisner, Martin Lauer, Christian Igel, Martin A. Riedmiller |
| 2007 | ICCBR | An Analysis of Case-Based Value Function Approximation by Approximating State Transition Graphs. | Thomas Gabel, Martin A. Riedmiller |
| 2007 | ICRA | Neural Reinforcement Learning Controllers for a Real Robot Application. | Roland Hafner, Martin A. Riedmiller |
| 2007 | KI | Making a Robot Learn to Play Soccer Using Reward and Punishment. | Heiko Mller, Martin Lauer, Roland Hafner, Sascha Lange, Artur Merke, Martin A. Riedmiller |
| 2006 | ESANN | Reducing policy degradation in neuro-dynamic programming. | Thomas Gabel, Martin A. Riedmiller |
| 2006 | RoboCup | Appearance-Based Robot Discrimination Using Eigenimages. | Sascha Lange, Martin A. Riedmiller |
| 2005 | ICCBR | CBR for State Value Function Approximation in Reinforcement Learning. | Thomas Gabel, Martin A. Riedmiller |
| 2005 | KI | Modeling Moving Objects in a Dynamically Changing Robot Application. | Martin Lauer, Sascha Lange, Martin A. Riedmiller |
| 2005 | SMC | Neural reinforcement learning to swing-up and balance a real pole. | Martin A. Riedmiller |
| 2005 | SMC | Comparing different methods to speed up reinforcement learning in a complex domain. | Martin A. Riedmiller, Daniel Withopf |
| 2005 | SMC | Learning policies for abstract state spaces. | Stephan Timmer, Martin A. Riedmiller |
| 2005 | RoboCup | Calculating the Perfect Match: An Efficient and Accurate Approach for Robot Self-localization. | Martin Lauer, Sascha Lange, Martin A. Riedmiller |
| 2004 | KI | Machine Learning for Autonomous Robots. | Martin A. Riedmiller |
| 2004 | RoboCup | Evolution of Computer Vision Subsystems in Robot Navigation and Image Classification Tasks. | Sascha Lange, Martin A. Riedmiller |
| 2003 | ICANN | Learning to Control at Multiple Time Scales. | Ralf Schoknecht, Martin A. Riedmiller |
| 2003 | IROS | Reinforcement learning on an omnidirectional mobile robot. | Roland Hafner, Martin A. Riedmiller |
| 2003 | IDA | The Smaller the Better: Comparison of Two Approaches for Sales Rate Prediction. | Martin Lauer, Martin A. Riedmiller, Thomas Ragg, Walter Baum, Michael Wigbers |
| 2003 | RoboCup | RoboCup: Yesterday, Today, and Tomorrow Workshop of the Executive Committee in Blaubeuren, October 2003. | Hans-Dieter Burkhard, Minoru Asada, Andrea Bonarini, Adam Jacoff, Daniele Nardi, Martin A. Riedmiller, Claude Sammut, Elizabeth Sklar, Manuela M. Veloso |
| 2003 | RoboCup | Overview of RoboCup 2003 Competition and Conferences. | Enrico Pagello, Emanuele Menegatti, Ansgar Bredenfeld, Paulo Costa, Thomas Christaller, Adam Jacoff, Jeffrey Johnson, Martin A. Riedmiller, Alessandro Saffiotti, Takashi Tomoichi |
| 2002 | ICANN | Speeding-up Reinforcement Learning with Multi-step Actions. | Ralf Schoknecht, Martin A. Riedmiller |
| 2001 | RoboCup | Karlsruhe Brainstormers - A Reinforcement Learning Approach to Robotic Soccer. | Artur Merke, Martin A. Riedmiller |
| 2000 | ICML | An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems. | Martin Lauer, Martin A. Riedmiller |
| 2000 | PRICAI | Learning Situation Dependent Success Rates of Actions in a RoboCup Scenario. | Sebastian Buck, Martin A. Riedmiller |
| 2000 | RoboCup | Karlsruhe Brainstormers 2000 Team Description. | Martin A. Riedmiller, Artur Merke, David Meier, Andreas Hoffmann, Alex Sinner, Ortwin Thate |
| 2000 | RoboCup | Karlsruhe Brainstormers - A Reinforcement Learning Approach to Robotic Soccer. | Martin A. Riedmiller, Artur Merke, David Meier, Andreas Hoffmann, Alex Sinner, Ortwin Thate, R. Ehrmann |
| 1999 | ICML | Distributed Value Functions. | Jeff G. Schneider, Weng-Keen Wong, Andrew W. Moore, Martin A. Riedmiller |
| 1999 | IJCAI | A Neural Reinforcement Learning Approach to Learn Local Dispatching Policies in Production Scheduling. | Simone C. Riedmiller, Martin A. Riedmiller |
| 1999 | RoboCup | Karlsruhe Brainstormers - Design Principles. | Martin A. Riedmiller, Sebastian Buck, Artur Merke, R. Ehrmann, Ortwin Thate, S. Dilger, Alex Sinner, Andreas Hoffmann, Lutz Frommberger |
| 1997 | ESANN | Application of a self-learning controller with continuous control signals based on the DOE-approach. | Martin A. Riedmiller |