Martin A. Riedmiller

Martin Lauer, Martin A. Riedmiller, M. Lauer, 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

Speeding-up Reinforcement Learning with Multi-step Actions

Martin A. Riedmiller, Ralf Schoknecht, 2002, ICANN.

Taming the reservoir: Feedforward training for recurrent neural networks

Oliver Obst, Martin A. Riedmiller, Oliver Obst, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

Learning to dribble on a real robot by success and failure

Martin Lauer, Martin A. Riedmiller, Roland Hafner, 2008, 2008 IEEE International Conference on Robotics and Automation.

Computational object recognition: a biologically motivated approach

Martin A. Riedmiller, Tim C. Kietzmann, Sascha Lange, 2009, Biological Cybernetics.

Learning and Transfer of Modulated Locomotor Controllers pdf

Yuval Tassa, Martin A. Riedmiller, David Silver, 2016, ArXiv.

09371 Abstracts Collection - Algorithmic Methods for Distributed Cooperative Systems

Subhash Suri, Sándor P. Fekete, Martin A. Riedmiller, 2009, Algorithmic Methods for Distributed Cooperative Systems.

Improving Deep Neural Networks with Probabilistic Maxout Units pdf

Martin A. Riedmiller, Jost Tobias Springenberg, J. T. Springenberg, 2013, ICLR.

Efficient Identification of State in Reinforcement Learning

Stephan Timmer, Martin A. Riedmiller, 2009, Künstliche Intell..

CBR for State Value Function Approximation in Reinforcement Learning

Martin A. Riedmiller, Thomas Gabel, T. Gabel, 2005, ICCBR.

Increasing Precision of Credible Case-Based Inference

Martin A. Riedmiller, Thomas Gabel, T. Gabel, 2008, ECCBR.

Multi-agent Case-Based Reasoning for Cooperative Reinforcement Learners

Martin A. Riedmiller, Thomas Gabel, T. Gabel, 2006, ECCBR.

Evolution of Computer Vision Subsystems in Robot Navigation and Image Classification Tasks

Martin A. Riedmiller, Sascha Lange, S. Lange, 2004, RoboCup.

Reinforcement Learning Using a Grid Based Function Approximator

Martin A. Riedmiller, Artur Merke, Alexander Sung, 2005, Biomimetic Neural Learning for Intelligent Robots.

Making a Robot Learn to Play Soccer Using Reward and Punishment

Martin Lauer, Martin A. Riedmiller, Heiko Müller, 2007, KI.

Fast Network Pruning and Feature Extraction by using the Unit-OBS Algorithm

Martin A. Riedmiller, Achim Stahlberger, A. Stahlberger, 1996, NIPS.

Reinforcement learning on an omnidirectional mobile robot

Martin A. Riedmiller, Roland Hafner, Roland Hafner, 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

The Neuro Slot Car Racer: Reinforcement Learning in a Real World Setting

Martin A. Riedmiller, Tim C. Kietzmann, T. Kietzmann, 2009, 2009 International Conference on Machine Learning and Applications.

Optimization of Gaussian process hyperparameters using Rprop

Martin A. Riedmiller, Manuel Blum, Manuel Blum, 2013, ESANN.

RoboCup-2003: New Scientific and Technical Advances

Alessandro Saffiotti, Emanuele Menegatti, Adam Jacoff, 2004, AI Mag..

DeepMind Control Suite pdf

Yuval Tassa, Martin A. Riedmiller, Yazhe Li, 2018, ArXiv.

Autonomous reinforcement learning on raw visual input data in a real world application

Martin A. Riedmiller, Sascha Lange, Arne Voigtländer, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

Approximate model-assisted Neural Fitted Q-Iteration

Martin A. Riedmiller, Thomas Lampe, T. Lampe, 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

Learn to Swing Up and Balance a Real Pole Based on Raw Visual Input Data

Martin A. Riedmiller, Sascha Lange, Jan Mattner, 2012, ICONIP.

Data-efficient Deep Reinforcement Learning for Dexterous Manipulation pdf

Yuval Tassa, Martin A. Riedmiller, Thomas Lampe, 2017, ArXiv.

Reinforcement learning for DEC-MDPs with changing action sets and partially ordered dependencies

Martin A. Riedmiller, Thomas Gabel, T. Gabel, 2008, AAMAS.

Real-time 3D Ball Recognition using Perspective and Catadioptric Cameras

Martin Lauer, Martin A. Riedmiller, Sascha Lange, 2007, EMCR.

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

Klaus Obermayer, Wendelin Böhmer, Martin A. Riedmiller, 2015, KI - Künstliche Intelligenz.

Striving for Simplicity: The All Convolutional Net pdf

Thomas Brox, Martin A. Riedmiller, Alexey Dosovitskiy, 2014, ICLR.

A Neural Approach for the Control of Piezoelectric Micromanipulation Robots

Martin A. Riedmiller, Karoly Santa, Michael Mews, 1998, J. Intell. Robotic Syst..

Scaling Adaptive Agent-Based Reactive Job-Shop Scheduling to Large-Scale Problems

Martin A. Riedmiller, Thomas Gabel, T. Gabel, 2007, 2007 IEEE Symposium on Computational Intelligence in Scheduling.

Overview of RoboCup 2003 Competition and Conferences

Alessandro Saffiotti, Emanuele Menegatti, Adam Jacoff, 2003, RoboCup.

Neural Fitted Q Iteration - First Experiences with a Data Efficient Neural Reinforcement Learning Method

Martin A. Riedmiller, 2005, ECML.

Neural reinforcement learning to swing-up and balance a real pole

Martin A. Riedmiller, 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

On Experiences in a Complex and Competitive Gaming Domain: Reinforcement Learning Meets RoboCup

Martin A. Riedmiller, Thomas Gabel, T. Gabel, 2007, 2007 IEEE Symposium on Computational Intelligence and Games.

Electricity Demand Forecasting using Gaussian Processes

Martin A. Riedmiller, Manuel Blum, 2013, AAAI Workshop: Trading Agent Design and Analysis.

A brain-computer interface for high-level remote control of an autonomous, reinforcement-learning-based robotic system for reaching and grasping

Tonio Ball, Martin A. Riedmiller, Thomas Lampe, 2014, IUI.

Learning machines that perceive, act and communicate

Martin A. Riedmiller, 2013, MLIS '13.

Maximum a Posteriori Policy Optimisation pdf

Yuval Tassa, Rémi Munos, Martin A. Riedmiller, 2018, ICLR.

Karlsruhe Brainstormers 2000 Team Description

Martin A. Riedmiller, David Meier, Artur Merke, 2000, RoboCup.

A Neural Reinforcement Learning Approach to Learn Local Dispatching Policies in Production Scheduling

Martin A. Riedmiller, Simone C. Riedmiller, 1999, IJCAI.

The Smaller the Better: Comparison of Two Approaches for Sales Rate Prediction

Martin Lauer, Martin A. Riedmiller, Thomas Ragg, 2003, IDA.

Deep learning of visual control policies

Martin A. Riedmiller, Sascha Lange, S. Lange, 2010, ESANN.

Fynesse: An architecture for integrating prior knowledge in autonomously learning agents

Martin Spott, Martin A. Riedmiller, Ralf Schoknecht, 2004, Soft Comput..

10 Steps and Some Tricks to Set up Neural Reinforcement Controllers

Martin A. Riedmiller, 2012, Neural Networks: Tricks of the Trade.

Learning an Embedding Space for Transferable Robot Skills

Karol Hausman, Martin A. Riedmiller, Jost Tobias Springenberg, 2018, ICLR.

An Analysis of Case-Based Value Function Approximation by Approximating State Transition Graphs

Martin A. Riedmiller, Thomas Gabel, T. Gabel, 2007, ICCBR.

Learning Situation Dependent Success Rates of Actions in a RoboCup Scenario

Martin A. Riedmiller, Sebastian Buck, Sebastian Buck, 2000, PRICAI.

Cognitive concepts in autonomous soccer playing robots

Martin Lauer, Martin A. Riedmiller, Roland Hafner, 2010, Cognitive Systems Research.

Emergence of Locomotion Behaviours in Rich Environments pdf

Yuval Tassa, Martin A. Riedmiller, David Silver, 2017, ArXiv.

Reinforcement learning on explicitly specified time scales

Martin A. Riedmiller, Ralf Schoknecht, Ralf Schoknecht, 2003, Neural Computing & Applications.

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards pdf

Martin A. Riedmiller, Thomas Lampe, Olivier Pietquin, 2017, ArXiv.

Self-supervised Learning of Image Embedding for Continuous Control pdf

Martin A. Riedmiller, Jost Tobias Springenberg, Nicolas Heess, 2019, ArXiv.

Machine Learning for Autonomous Robots

Martin A. Riedmiller, 2004, KI.

Controlling biological neural networks with deep reinforcement learning

Martin A. Riedmiller, Joschka Boedecker, Ulrich Egert, 2018, ESANN.

Learning by Playing - Solving Sparse Reward Tasks from Scratch

Martin A. Riedmiller, Thomas Lampe, Jost Tobias Springenberg, 2018, ICML.

Learning to Drive a Real Car in 20 Minutes

Martin A. Riedmiller, Michael Montemerlo, Hendrik Dahlkamp, 2007, 2007 Frontiers in the Convergence of Bioscience and Information Technologies.

Discriminative Unsupervised Feature Learning with Convolutional Neural Networks

Thomas Brox, Martin A. Riedmiller, Alexey Dosovitskiy, 2014, NIPS.

Deterministic Policy Gradient Algorithms

Guy Lever, Martin A. Riedmiller, David Silver, 2014, ICML.

06251 Abstracts Collection - Multi-Robot Systems: Perception, Behaviors, Learning, and Action

Uwe Schwiegelshohn, Manuela M. Veloso, Martin A. Riedmiller, 2006, Multi-Robot Systems: Perception, Behaviors, Learning, and Action.

Approximate real-time optimal control based on sparse Gaussian process models

Martin A. Riedmiller, Jost Tobias Springenberg, Joschka Boedecker, 2014, 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL).

A learned feature descriptor for object recognition in RGB-D data

Martin A. Riedmiller, Jost Tobias Springenberg, Manuel Blum, 2012, 2012 IEEE International Conference on Robotics and Automation.

Unsupervised Learning of Local Features for Music Classification

Martin A. Riedmiller, Jan Wülfing, Jan Wülfing, 2012, ISMIR.

A Case Study on Improving Defense Behavior in Soccer Simulation 2D: The NeuroHassle Approach

Martin A. Riedmiller, Thomas Gabel, Florian Trost, 2009, RoboCup.

Reinforcement Learning for Cooperating and Communicating Reactive Agents in Electrical Power Grids

Andrew W. Moore, Jeff G. Schneider, Martin A. Riedmiller, 2000, Balancing Reactivity and Social Deliberation in Multi-Agent Systems.

Concepts and Facilities of a Neural Reinforcement Learning Control Architecture for Technical Process Control

Martin A. Riedmiller, 1999, Neural Computing & Applications.

Learning to Control at Multiple Time Scales

Martin A. Riedmiller, Ralf Schoknecht, 2003, ICANN.

Modeling Moving Objects in a Dynamically Changing Robot Application

Martin Lauer, Martin A. Riedmiller, Sascha Lange, 2005, KI.

Selbständig lernende neuronale Steuerungen

Martin A. Riedmiller, 1997 .

Relative Entropy Regularized Policy Iteration pdf

Yuval Tassa, Steven Bohez, Martin A. Riedmiller, 2018, ArXiv.

Deep auto-encoder neural networks in reinforcement learning

Martin A. Riedmiller, Sascha Lange, S. Lange, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Graph networks as learnable physics engines for inference and control pdf

Raia Hadsell, Martin A. Riedmiller, Jost Tobias Springenberg, 2018, ICML.

Playing Atari with Deep Reinforcement Learning pdf

Alex Graves, Koray Kavukcuoglu, Martin A. Riedmiller, 2013, ArXiv.

Improvement of a Web Browser Game Through the Knowledge Extracted from Player Behavior

José Neves, João Alves, Martin A. Riedmiller, 2013, KICSS.

Incremental GRLVQ: Learning relevant features for 3D object recognition

Martin A. Riedmiller, Tim C. Kietzmann, Sascha Lange, 2008, Neurocomputing.

Karlsruhe Brainstormers - A Reinforcement Learning Approach to Robotic Soccer

Martin A. Riedmiller, Artur Merke, A. Merke, 2000, Robot Soccer World Cup.

Adaptive long-term control of biological neural networks with Deep Reinforcement Learning

Martin A. Riedmiller, Joschka Boedecker, Ulrich Egert, 2019, Neurocomputing.

Comparing different methods to speed up reinforcement learning in a complex domain

Martin A. Riedmiller, Daniel Withopf, D. Withopf, 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

Die Brainstormers: Entwurfsprinzipien lernfähiger autonomer Roboter

Martin Lauer, Martin A. Riedmiller, Roland Hafner, 2006, Informatik-Spektrum.

On Progress in RoboCup: The Simulation League Showcase

Martin A. Riedmiller, Thomas Gabel, 2010, RoboCup.

Neural Reinforcement Learning Controllers for a Real Robot Application

Martin A. Riedmiller, Roland Hafner, Roland Hafner, 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

Appearance-Based Robot Discrimination Using Eigenimages

Martin A. Riedmiller, Sascha Lange, 2006, RoboCup.

Reinforcement learning in a nutshell

Christian Igel, Martin A. Riedmiller, Verena Heidrich-Meisner, 2007, ESANN.

Enhancing the episodic natural actor-critic algorithm by a regularisation term to stabilize learning of control structures

Kurt Geihs, Martin A. Riedmiller, Roland Reichle, 2011, 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL).

Effective Methods for Reinforcement Learning in Large Multi-Agent Domains (Leistungsfähige Verfahren für das Reinforcement Lernen in komplexen Multi-Agenten-Umgebungen)

Martin A. Riedmiller, Daniel Withopf, D. Withopf, 2005, it Inf. Technol..

Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images

Martin A. Riedmiller, Jost Tobias Springenberg, Joschka Boedecker, 2015, NIPS.

Application of a self-learning controller with continuous control signals based on the DOE-approach

Martin A. Riedmiller, 1997, ESANN.

Autonomous Optimization of Targeted Stimulation of Neuronal Networks

Martin A. Riedmiller, Joschka Boedecker, Ulrich Egert, 2016, PLoS Comput. Biol..

Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks

Thomas Brox, Martin A. Riedmiller, Alexey Dosovitskiy, 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Learning a Partial Behavior for a Competitive Robotic Soccer Agent

Martin A. Riedmiller, Thomas Gabel, 2006, Künstliche Intell..

Improved neural fitted Q iteration applied to a novel computer gaming and learning benchmark

Martin A. Riedmiller, Thomas Gabel, Christian Lutz, 2011, 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL).

Learning Temporal Coherent Features through Life-Time Sparsity

Martin A. Riedmiller, Jost Tobias Springenberg, 2012, ICONIP.

Reinforcement learning for robot soccer

Martin A. Riedmiller, Roland Hafner, Sascha Lange, 2009, Auton. Robots.

Karlsruhe Brainstormers - A Reinforcement Learning Approach to Robotic Soccer

Martin A. Riedmiller, David Meier, Artur Merke, 2000, RoboCup.

Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup pdf

Murilo F. Martins, Martin A. Riedmiller, Thomas Lampe, 2019, Robotics: Science and Systems.

Towards Real Robot Learning in the Wild: A Case Study in Bipedal Locomotion

Martin A. Riedmiller, Viorica Patraucean, Nicolas Heess, 2021, CoRL.

A Constrained Multi-Objective Reinforcement Learning Framework

Sandy H. Huang, Yuval Tassa, Raia Hadsell, 2021, CoRL.

Reinforcement learning in feedback control

Martin A. Riedmiller, Martin Riedmiller, Roland Hafner, 2011, Machine Learning.

Evaluation of Policy Gradient Methods and Variants on the Cart-Pole Benchmark

Martin A. Riedmiller, S. Schaal, J. Peters, 2007, 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning.

Human-level control through deep reinforcement learning

Shane Legg, Alex Graves, Marc G. Bellemare, 2015, Nature.

Regularized Hierarchical Policies for Compositional Transfer in Robotics pdf

Martin A. Riedmiller, Thomas Lampe, Abbas Abdolmaleki, 2019, ArXiv.

Design of autonomously learning controllers using FYNESSE

Martin Spott, Martin A. Riedmiller, Martin Riedmiller, 2001 .

A Distributional View on Multi-Objective Policy Optimization

H. Francis Song, Sandy H. Huang, Martina Zambelli, 2020, ICML.

High Quality Thermostat Control by Reinforcement Learning - A Case Study

Martin A. Riedmiller, Martin Riedmiller, 1998 .

Learning to Control Dynamic Systems

Martin A. Riedmiller, 1996 .

Using reinforcement learning for engine control

Martin A. Riedmiller, Martin Riedmiller, Ralf Schoknecht, 1999 .

Rprop - Description and Implementation Details

Martin A. Riedmiller, Martin Riedmiller, 1994 .

BRIDGING THE GAP : LEARNING IN THE ROBOCUP SIMULATION AND MIDSIZE LEAGUE

Martin A. Riedmiller, Roland Hafner, S. Lange, 2006 .

ADAPTIVE REACTIVE JOB-SHOP SCHEDULING WITH REINFORCEMENT LEARNING AGENTS

Martin A. Riedmiller, Martin Riedmiller, Thomas Gabel, 2008 .

Application of sequential reinforcement learning to control dynamic systems

Martin A. Riedmiller, Martin Riedmiller, 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

Batch Reinforcement Learning

Martin A. Riedmiller, Martin Riedmiller, Sascha Lange, 2012, Reinforcement Learning.

Line Based Robot Localization under Natural Light Conditions

Martin A. Riedmiller, A. Merke, Stefan Welker, 2004 .

RPROP - A Fast Adaptive Learning Algorithm

Martin A. Riedmiller, Heinrich Braun, Martin Riedmiller, 1992 .

Learning Symbolic Maps from Robot Navigation Bachelor Thesis in Cognitive Science

Martin A. Riedmiller, Ute Schmid, Martin Riedmiller, 2004 .

RoboCup 2004: Robot Soccer World Cup VIII

Martin A. Riedmiller, José Santos-Victor, Martin Riedmiller, 2005, RoboCup.

Fynesse: a hybrid architecture for self-learning control

Martin Spott, Martin A. Riedmiller, Martin Riedmiller, 2000 .

Abstract State Spaces with History

Martin A. Riedmiller, S. Timmer, 2006, NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society.

The Cooperative Driver: Multi-Agent Learning for Preventing Traffic Jams

Martin A. Riedmiller, Martin Riedmiller, Thomas Gabel, 2013 .

Massively Parallel Training of Multi Layer Perceptrons With Irregular Topologies

Martin A. Riedmiller, Heinrich Braun, Martin Riedmiller, 1995, ICANNGA.

Advanced supervised learning in multi-layer perceptrons — From backpropagation to adaptive learning algorithms

Martin A. Riedmiller, Martin Riedmiller, 1994 .

Distributed policy search reinforcement learning for job-shop scheduling tasks

Martin A. Riedmiller, Martin Riedmiller, Thomas Gabel, 2012 .

Controlling an inverted pendulum by neural plant identification

Martin A. Riedmiller, Martin Riedmiller, 1993, Proceedings of IEEE Systems Man and Cybernetics Conference - SMC.

Brainstormers 2D — Team Description 2009

Martin A. Riedmiller, Martin Riedmiller, Thomas Gabel, 2005 .

Learning by Experience from Others — Social Learning and Imitation in Animals and Robots

Martin A. Riedmiller, Martin Riedmiller, Artur Merke, 2003 .

Multimodal deep learning for robust RGB-D object recognition

Wolfram Burgard, Andreas Eitel, Martin A. Riedmiller, 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

First results on the application of the Fynesse control architecture

Martin Spott, Martin A. Riedmiller, Martin Riedmiller, 1997, 1997 IEEE Aerospace Conference.

Approaches for the integration of a priori knowledge into anautonomously learning control

Martin A. Riedmiller, Ralf Schoknecht, architectureMartin Spott, 1999 .

Search space reduction for strategy learning in sequential decision processes

Martin A. Riedmiller, Ralf Schoknecht, M. Spott, 1999, ICONIP'99. ANZIIS'99 & ANNES'99 & ACNN'99. 6th International Conference on Neural Information Processing. Proceedings (Cat. No.99EX378).

Modeling effects of intrinsic and extrinsic rewards on the competition between striatal learning systems

Martin A. Riedmiller, T. Lampe, J. Boedecker, 2013, Front. Psychol..

Compositional Transfer in Hierarchical Reinforcement Learning

Martin A. Riedmiller, Jost Tobias Springenberg, N. Heess, 2019, Robotics: Science and Systems.

A Vision for Reinforcement Learning and its Implications for Neural Compuation

Martin A. Riedmiller, S. Lange, 2010 .

Evaluation of Machine Learning Methods on a Swinging Humanoid

Martin A. Riedmiller, Roland Hafner, T. Lampe, 2009 .

RoboCup 2003 : New Scientific and Technical Advancements

Martin A. Riedmiller, E. Sklar, D. Polani, 2004 .

Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach

Matthew W. Hoffman, Martin A. Riedmiller, Jost Tobias Springenberg, 2022, ArXiv.

Position States Learning t t-1 t-2 t-3 pull push Velocity States Obs

Martin A. Riedmiller, Roland Hafner, Rico Jonschkowski, 2017 .

On the Applicability of Unsupervised Feature Learning for Object Recognition in RGB-D Data

Martin A. Riedmiller, Jost Tobias Springenberg, Martin Riedmiller, 2011 .

De Novo Molecular Design Using Graph Kernels

Martin A. Riedmiller, R. Backofen, M. Dragoş, 2011 .

Äääöòòòò Øó Óòøöóð Ø Åùðøøôðð Ììññ Ë Blockin Blockinðð×

Martin A. Riedmiller, 2007 .

A New Method for the Analysis of Neural Reference

Martin A. Riedmiller, Michael Wigbers, 2007 .

Lecture Notes in Artificial Intelligence (Subseries of Lecture NoteS in Computer Science): Preface

Martin A. Riedmiller, José Santos-Victor, Daniele Nardi, 2005 .

Data-efficient Hindsight Off-policy Option Learning

Martin A. Riedmiller, N. Heess, Roland Hafner, 2020, ICML.

Participating in Autonomous Robot Competitions: Experiences from a Robot Soccer Team

Martin A. Riedmiller, M. Lauer, 2009 .

MO2: Model-Based Offline Options

Martin A. Riedmiller, R. Hadsell, N. Heess, 2022, CoLLAs.

Solving Continuous Control via Q-learning

Martin A. Riedmiller, Markus Wulfmeier, Wilko Schwarting, 2022, ICLR.

SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration

Martin A. Riedmiller, N. Heess, Roland Hafner, 2022, ArXiv.

SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration

Martin A. Riedmiller, N. Heess, Roland Hafner, 2022, arXiv.org.

Speeding-up Reinforcement Learning with

Martin A. Riedmiller, Martin Riedmiller, 2001 .

Fuzzy-neuro-controlled verified instruction scheduler

Martin Spott, Martin A. Riedmiller, Thilo S. Gaul, 1999, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).

Reinforcement learning without an explicit terminal state

Martin A. Riedmiller, Martin Riedmiller, 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

Self-learning neural control of a mobile robot

Martin A. Riedmiller, B. Janusz, M. Riedmiller, 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

Using Neural Reinforcement Controllers in Robotics

Martin A. Riedmiller, Martin Riedmiller, Barbara Janusz, 1995 .

Generalization in Reinfor ement Learning and theUse of Observations-Based

Martin A. Riedmiller, L. Lauer, 2007 .

Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies pdf

Igor Gilitschenski, Martin A. Riedmiller, Daniela Rus, 2021, NeurIPS.

Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics

Martin A. Riedmiller, Thomas Lampe, Jonas Buchli, 2020, CoRL.

A direct adaptive method for faster backpropagation learning: the RPROP algorithm

Martin A. Riedmiller, Heinrich Braun, Martin Riedmiller, 1993, IEEE International Conference on Neural Networks.

Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes pdf

Raia Hadsell, Martin A. Riedmiller, Claudio Fantacci, 2021, CoRL.

Magnetic control of tokamak plasmas through deep reinforcement learning

Martin A. Riedmiller, Brendan D. Tracey, Timo Ewalds, 2022, Nature.

Using Machine Learning Techniques in Complex Multi-Agent Domains

Martin A. Riedmiller, Martin Riedmiller, Artur Merke, 2003 .

Evaluating model-based planning and planner amortization for continuous control pdf

Yuval Tassa, Alessandro Davide Ialongo, Martin A. Riedmiller, 2021, ICLR.

Collect & Infer - a fresh look at data-efficient Reinforcement Learning

Martin A. Riedmiller, Jost Tobias Springenberg, Nicolas Heess, 2021, CoRL.

On Multi-objective Policy Optimization as a Tool for Reinforcement Learning pdf

Sandy H. Huang, Raia Hadsell, Martin A. Riedmiller, 2021, ArXiv.

Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion pdf

Martin A. Riedmiller, Martin Riedmiller, Markus Wulfmeier, 2020, CoRL.

Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning pdf

Martin A. Riedmiller, Thomas Lampe, Abbas Abdolmaleki, 2020, ICLR.

The Challenges of Exploration for Offline Reinforcement Learning

Martin A. Riedmiller, William F. Whitney, Markus Wulfmeier, 2022, ArXiv.

SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration

Martin A. Riedmiller, N. Heess, Roland Hafner, 2022, ArXiv.

Robust Reinforcement Learning for Continuous Control with Model Misspecification pdf

Martin A. Riedmiller, Nir Levine, Abbas Abdolmaleki, 2019, ICLR.

V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control pdf

H. Francis Song, Martin A. Riedmiller, Abbas Abdolmaleki, 2019, ICLR.

Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration pdf

Martin A. Riedmiller, Nicolas Heess, Martin Riedmiller, 2021, ArXiv.

Rethinking Exploration for Sample-Efficient Policy Learning

Martin A. Riedmiller, Jost Tobias Springenberg, Martin Riedmiller, 2021, ArXiv.

Decoupled Exploration and Exploitation Policies for Sample-Efficient Reinforcement Learning

Martin A. Riedmiller, Kyunghyun Cho, Abbas Abdolmaleki, 2021, 2101.09458.

Speeding-up Reinforcement Learning with Multi-step Actions

Martin A. Riedmiller, Ralf Schoknecht, 2002, ICANN.

Representation Matters: Improving Perception and Exploration for Robotics pdf

Ankush Gupta, Martin A. Riedmiller, Thomas Lampe, 2020, 2021 IEEE International Conference on Robotics and Automation (ICRA).

A Generalist Dynamics Model for Control

Martin A. Riedmiller, N. Heess, Leonard Hasenclever, 2023, ArXiv.

Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains

Martin A. Riedmiller, N. Heess, A. Abdolmaleki, 2023, ArXiv.

On a Successful Application of Multi-Agent Reinforcement Learning to Operations Research Benchmarks

Martin A. Riedmiller, M. Riedmiller, T. Gabel, 2007, 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning.

Local Search for Policy Iteration in Continuous Control pdf

Jackie Kay, Yuval Tassa, Martin A. Riedmiller, 2020, ArXiv.

Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models

Martin A. Riedmiller, Thomas Lampe, Abbas Abdolmaleki, 2019, CoRL.

Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification pdf

Martin A. Riedmiller, Sumanth Dathathri, Timothy A. Mann, 2020, ArXiv.

Learning Robot Skill Embeddings

Karol Hausman, Martin A. Riedmiller, Jost Tobias Springenberg, 2017, NIPS 2017.

Learning Skill Embeddings for Transferable Robot Skills

Martin A. Riedmiller, Jost Tobias Springenberg, Ziyu Wang, 2017, NIPS 2017.

EARNING AN E MBEDDING S PACE FOR T RANSFERABLE R OBOT S KILLS

Martin A. Riedmiller, Ziyun Wang, N. Heess, 2018 .

Learning Situation Dependent Success Rates of Actions in a RoboCup Scenario

Martin A. Riedmiller, Sebastian Buck, 2000, PRICAI.

Towards A Unified Agent with Foundation Models

Martin A. Riedmiller, Norman Di Palo, N. Heess, 2023, ArXiv.

Data-efficient Deep Reinforcement Learning for Dexterous Manipulation

Martin A. Riedmiller, T. Lillicrap, N. Heess, 2017, ArXiv.

Preparative continuous separation of biological particles by means of free‐flow magnetophoresis in a free‐flow electrophoresis chamber

Martin A. Riedmiller, C Cremer, R Hartig, 1992, Electrophoresis.

Learning to Control at Multiple Time Scales

Martin A. Riedmiller, Ralf Schoknecht, 2003, ICANN.

On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning

Sandy H. Huang, Martin A. Riedmiller, R. Hadsell, 2021, 2106.08199.

Quinoa: a Q-function You Infer Normalized Over Actions pdf

Martin A. Riedmiller, Abbas Abdolmaleki, Jost Tobias Springenberg, 2019, ArXiv.

Reinforcement Learning for Cooperating and Communicating Reactive Agents in Electrical Power Grids

Martin A. Riedmiller, A. Moore, J. Schneider, 2000, Balancing Reactivity and Social Deliberation in Multi-Agent Systems.

Faster sorting algorithms discovered using deep reinforcement learning

Martin A. Riedmiller, Oriol Vinyals, D. Hassabis, 2023, Nature.

Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World

Martin A. Riedmiller, Barnabas Gavin Cangan, B. Schölkopf, 2023, ArXiv.

Human-level control through deep reinforcement learning

Marc G. Bellemare, Martin A. Riedmiller, Andrei A. Rusu, 2015, Nature.

Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks

Martin A. Riedmiller, T. Brox, A. Dosovitskiy, 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Discriminative Unsupervised Feature Learning with Convolutional Neural Networks

Martin A. Riedmiller, T. Brox, A. Dosovitskiy, 2014, NIPS.

A Generalist Dynamics Model for Control

Martin A. Riedmiller, N. Heess, Leonard Hasenclever, 2023, ArXiv.

Policy composition in reinforcement learning via multi-objective policy optimization

Martin A. Riedmiller, N. Heess, Doina Precup, 2023, ArXiv.

Solving Continuous Control via Q-learning

Martin A. Riedmiller, Markus Wulfmeier, Wilko Schwarting, 2022, ICLR.

Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks

Martin A. Riedmiller, T. Brox, A. Dosovitskiy, 2022 .

Towards practical reinforcement learning for tokamak magnetic control

Martin A. Riedmiller, Brendan D. Tracey, Timo Ewalds, 2023, ArXiv.

RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation

Oleg O. Sushkov, Sergio Gomez Colmenarejo, Martin A. Riedmiller, 2023, 2306.11706.

Striving for Simplicity: The All Convolutional Net

Martin A. Riedmiller, T. Brox, J. T. Springenberg, 2014, ICLR.