World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
54
Citations
68659
World Ranking
4416
National Ranking
271

Overview

Martin Riedmiller is affiliated with DeepMind in the United Kingdom and is active in the field of computer science, particularly focusing on artificial intelligence and control systems. Their research spans various subfields including computational theory and mathematics, computer vision and pattern recognition, and biomedical engineering.

Their main areas of work encompass topics such as reinforcement learning in robotics, robot manipulation and learning, adversarial robustness in machine learning, magnetic confinement fusion research, evolutionary algorithms and applications, smart grid energy management, and adaptive dynamic programming control.

Frequent co-authors who have collaborated extensively with Martin Riedmiller include Nicolas Heess, Abbas Abdolmaleki, Markus Wulfmeier, Jost Tobias Springenberg, and Roland Hafner.

Martin Riedmiller's publication record includes a substantial number of articles in prominent venues. The most common publication platforms are:

  • arXiv (Cornell University) with 45 publications
  • Nature, featuring 2 publications
  • Fusion Engineering and Design, with 1 publication
  • Science, with 1 publication

Selected recent papers encompass diverse contributions between 2020 and 2023:

  • "Magnetic control of tokamak plasmas through deep reinforcement learning," 2022, Nature
  • "Faster sorting algorithms discovered using deep reinforcement learning," 2023, Nature
  • "Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning," 2020, arXiv (Cornell University)
  • "Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics," 2020, arXiv (Cornell University)

This profile summarizes a scientific career focused on advanced computational methods and their application to robotics and control systems, with a notable emphasis on reinforcement learning methodologies. Their investigations intersect practical robotics challenges and theoretical aspects of machine learning.

Best Publications

  • Human-level control through deep reinforcement learning

    Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Andrei A. Rusu

  • Playing Atari with Deep Reinforcement Learning

    Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Alex Graves

  • A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm.

    Riedmiller M;Braun H

  • Striving for Simplicity: The All Convolutional Net

    Jost Tobias Springenberg;Alexey Dosovitskiy;Thomas Brox;Martin A. Riedmiller

  • Deterministic Policy Gradient Algorithms

    David Silver;Guy Lever;Nicolas Heess;Thomas Degris

  • Neural fitted q iteration – first experiences with a data efficient neural reinforcement learning method

    Martin Riedmiller

  • Discriminative Unsupervised Feature Learning with Convolutional Neural Networks

    Alexey Dosovitskiy;Jost Tobias Springenberg;Martin Riedmiller;Thomas Brox

  • Emergence of Locomotion Behaviours in Rich Environments

    Nicolas Heess;Dhruva Tb;Srinivasan Sriram;Jay Lemmon

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

    Martin Riedmiller

  • Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks

    Alexey Dosovitskiy;Philipp Fischer;Jost Tobias Springenberg;Martin Riedmiller

  • Multimodal deep learning for robust RGB-D object recognition

    Andreas Eitel;Jost Tobias Springenberg;Luciano Spinello;Martin Riedmiller

  • DeepMind Control Suite

    Yuval Tassa;Yotam Doron;Alistair Muldal;Tom Erez

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

    Matej Vecerík;Todd Hester;Jonathan Scholz;Fumin Wang

  • Embed to control: a locally Linear Latent dynamics model for control from raw images

    Manuel Watter;Jost Tobias Springenberg;Joschka Boedecker;Martin Riedmiller

  • An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems

    Martin Lauer;Martin A. Riedmiller

  • RPROP - a fast adaptive learning algorithm

    Martin Riedmiller;Heinrich Braun

  • Batch Reinforcement Learning

    Sascha Lange;Thomas Gabel;Martin A. Riedmiller

  • Deep auto-encoder neural networks in reinforcement learning

    Sascha Lange;Martin Riedmiller

  • Graph Networks as Learnable Physics Engines for Inference and Control

    Alvaro Sanchez-Gonzalez;Nicolas Heess;Jost Tobias Springenberg;Josh Merel

  • Reinforcement learning for robot soccer

    Martin Riedmiller;Thomas Gabel;Roland Hafner;Sascha Lange

  • Maximum a Posteriori Policy Optimisation

    Abbas Abdolmaleki;Jost Tobias Springenberg;Yuval Tassa;Rémi Munos

Frequent Co-Authors

Jost Tobias Springenberg
Jost Tobias Springenberg University of Freiburg
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Yuval Tassa
Yuval Tassa Google (United States)
Alexey Dosovitskiy
Alexey Dosovitskiy Google (United States)
Thomas Brox
Thomas Brox University of Freiburg
David Silver
David Silver DeepMind (United Kingdom)
Raia Hadsell
Raia Hadsell DeepMind (United Kingdom)
Daniele Nardi
Daniele Nardi Sapienza University of Rome
Volodymyr Mnih
Volodymyr Mnih DeepMind (United Kingdom)
Jonas Buchli
Jonas Buchli DeepMind (United Kingdom)

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Choosing to study Computer Science in the USA can open the door to a wide array of online degrees and career paths. Many students start by researching highly accredited online universities, as accreditation can significantly influence job prospects and further study opportunities.

For those passionate about interactive technology, video game programs provide cutting-edge skills in coding, animation, and digital storytelling. Cybersecurity is another booming field; earning an online master's degree cyber security can help you advance into high-demand roles protecting networks and data.

Computer Science graduates are also finding opportunities in industries like construction. Those interested in project management and building technologies can benefit from an affordable online construction management degree. With flexible online programs and a variety of specializations, students can shape a career path that aligns with their interests and the evolving tech landscape.

Best Scientists Citing Martin Riedmiller

Trending Scientists

Recently Published Articles