Martin Riedmiller

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

Human-level control through deep reinforcement learning

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

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.

Reinforcement learning in feedback control

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

Learning Robot Skill Embeddings

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

Data-efficient Hindsight Off-policy Option Learning pdf

Dushyant Rao, Thomas Lampe, Abbas Abdolmaleki, 2020, ArXiv.

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

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

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

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

First results on the application of the Fynesse control architecture

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

Creating a Robot Soccer Team from Scratch: the Brainstormers Tribots

Martin Lauer, Stefan Freitag, M. Fricke, 2003 .

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).

Robust Reinforcement Learning for Continuous Control with Model Misspecification pdf

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

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

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

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

Martin A. Riedmiller, Martin Riedmiller, 1994 .

Design of autonomously learning controllers using FYNESSE

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

Local Search for Policy Iteration in Continuous Control pdf

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

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

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

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

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

Rethinking Exploration for Sample-Efficient Policy Learning

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

Rprop - Description and Implementation Details

Martin A. Riedmiller, Martin Riedmiller, 1994 .

A new method for the analysis of neural reference model control

Martin Riedmiller, Michael Wigbers, 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

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).

RPROP - A Fast Adaptive Learning Algorithm

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

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

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

Brainstormers 2003 - Team Description

Martin Riedmiller, Artur Merke, Daniel Withopf, 2003 .

Compositional Transfer in Hierarchical Reinforcement Learning

Thomas Lampe, Abbas Abdolmaleki, Jost Tobias Springenberg, 2019, RSS 2020.

Controlling an inverted pendulum by neural plant identification

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

Simple Sensor Intentions for Exploration pdf

Jost Tobias Springenberg, Martin Riedmiller, Markus Wulfmeier, 2020, ArXiv.

Learning Symbolic Maps from Robot Navigation Bachelor Thesis in Cognitive Science

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

Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics

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

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

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

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

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

Data-efficient Hindsight Off-policy Option Learning pdf

Dushyant Rao, Abbas Abdolmaleki, Nicolas Heess, 2021, ICML.

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.

Decoupled Exploration and Exploitation Policies for Sample-Efficient Reinforcement Learning

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

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

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

Representation Matters: Improving Perception and Exploration for Robotics

Martin Riedmiller, Markus Wulfmeier, Roland Hafner, 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

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

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

Speeding-up Reinforcement Learning with

Martin A. Riedmiller, Martin Riedmiller, 2001 .

Learning ( near ) Time Optimal Control for Flexible Robot Joints

Martin Riedmiller, 2011 .

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

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

Daily Prediction of the Foreign Exchange Rate Between the US Dollar and the German Mark Using Neural

Martin Riedmiller, Steffen Gutjahr, 1997 .