Riedmiller, Martin

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 30,357

Playing Atari with Deep Reinforcement Learning

Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 9,471

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

Riedmiller, M., & Braun, H. (2002) · IEEE International Conference on Neural Networks

Neural Networks and ApplicationsStochastic Gradient Optimization Techniques

Cited by 3,917

Striving for Simplicity: The All Convolutional Net

Springenberg, J. T., Dosovitskiy, A., Brox, T., & Riedmiller, M. (2014) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 3,447

Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks

Dosovitskiy, A., Fischer, P., Springenberg, J. T., Riedmiller, M., & Brox, T. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 762

Magnetic control of tokamak plasmas through deep reinforcement learning

Degrave, J., Felici, F., Buchli, J., Neunert, M., Tracey, B., Carpanese, F., Ewalds, T., Hafner, R., Abdolmaleki, A., de las Casas, D., et al. (2022) · Nature

Magnetic confinement fusion researchFusion and Plasma Physics Studies

Cited by 751

Emergence of Locomotion Behaviours in Rich Environments

Heess, N., TB, D., Sriram, S., Lemmon, J., Merel, J., Wayne, G., Tassa, Y., Erez, T., Wang, Z., Eslami, S. M. A., et al. (2017) · arXiv

Reinforcement Learning in RoboticsRobotic Locomotion and Control

Cited by 657

DeepMind Control Suite

Tassa, Y., Doron, Y., Muldal, A., Erez, T., Li, Y., Casas, D. D. L., Budden, D., Abdolmaleki, A., Merel, J., Lefrancq, A., et al. (2018) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 497

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

Vecerik, M., Hester, T., Scholz, J., Wang, F., Pietquin, O., Piot, B., Heess, N., Rothörl, T., Lampe, T., & Riedmiller, M. (2017) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 478

Graph networks as learnable physics engines for inference and control

Sanchez-Gonzalez, A., Heess, N., Springenberg, J. T., Merel, J., Riedmiller, M., Hadsell, R., & Battaglia, P. (2018) · arXiv

Reinforcement Learning in RoboticsModel Reduction and Neural Networks

Cited by 387

Maximum a Posteriori Policy Optimisation

Abdolmaleki, A., Springenberg, J. T., Tassa, Y., Munos, R., Heess, N., & Riedmiller, M. (2018) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 259

Faster sorting algorithms discovered using deep reinforcement learning

Mankowitz, D. J., Michi, A., Zhernov, A., Gelmi, M., Selvi, M., Paduraru, C., Leurent, E., Iqbal, S., Lespiau, J.-B., Ahern, A., et al. (2023) · Nature

Evolutionary Algorithms and ApplicationsArtificial Intelligence in Games

Cited by 177

Towards A Unified Agent with Foundation Models

Di Palo, N., Byravan, A., Hasenclever, L., Wulfmeier, M., Heess, N., & Riedmiller, M. (2023) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 16

RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation

Bousmalis, K., Vezzani, G., Rao, D., Devin, C., Lee, A. X., Bauza, M., Davchev, T., Zhou, Y., Gupta, A., Raju, A., et al. (2023) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 13