Most cited papers in Reinforcement Learning in Robotics
What are the most cited papers in Reinforcement Learning in Robotics?
The most cited paper in Reinforcement Learning in Robotics is "Deep learning in neural networks: An overview" (Jürgen Schmidhuber, 2014), with 18,327 citations. Next come "Diagnosing Non-Intermittent Anomalies in Reinforcement Learning Policy Executions (Short Paper)" (11,361) and "Mastering the game of Go without human knowledge" (9,370).
Showing 50 of 97 papers. Every paper with more than 906 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Deep learning in neural networks: An overview Jürgen Schmidhuber · Neural Networks | 13 Oct 2014 | 18,327 |
| 2 | Diagnosing Non-Intermittent Anomalies in Reinforcement Learning Policy Executions (Short Paper) Esper, Khalil, Spieck, Jan, Sixdenier, Pierre-Louis et al. · arXiv (Cornell University) | 20 Jul 2017 | 11,361 |
| 3 | Mastering the game of Go without human knowledge David Lawrence Silver, Julian Schrittwieser, Karen Simonyan et al. · Nature | Oct 2017 | 9,370 |
| 4 | Q-learning Christopher J. C. H. Watkins, Peter Dayan · Machine Learning | May 1992 | 9,122 |
| 5 | Reinforcement Learning: A Survey Leslie Pack Kaelbling, Michael L. Littman, Andrew Moore · Journal of Artificial Intelligence Research | May 1996 | 8,975 |
| 6 | Reinforcement Learning: An Introduction Jeffrey D. Johnson, Jinghong Li, Zengshi Chen · Neurocomputing | Nov 2000 | 8,698 |
| 7 | Continuous control with deep reinforcement learning Timothy Lillicrap, Jonathan J. Hunt, Alexander Pritzel et al. · arXiv (Cornell University) | 22 Jul 2016 | 6,781 |
| 8 | Deep Reinforcement Learning with Double Q-Learning Hado van Hasselt, Arthur Guez, David Lawrence Silver · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) | 2 Mar 2016 | 6,325 |
| 9 | Finite-time Analysis of the Multiarmed Bandit Problem Peter L. Auer, Nicolò Cesa‐Bianchi, Paul Fischer · Machine Learning | May 2002 | 5,993 |
| 10 | Policy Gradient Methods for Reinforcement Learning with Function Approximation Richard S. Sutton, David McAllester, Satinder Pal Singh et al. | 29 Nov 1999 | 4,956 |
| 11 | Deep Reinforcement Learning: A Brief Survey Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage et al. · IEEE Signal Processing Magazine | Nov 2017 | 4,470 |
| 12 | Technical Note: Q-Learning Christopher J. C. H. Watkins, Peter Dayan · Machine Learning | May 1992 | 3,822 |
| 13 | A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play David Lawrence Silver, Thomas Hubert, Julian Schrittwieser et al. · Science | 6 Dec 2018 | 3,636 |
| 14 | Dynamic Programming and Markov Processes. Marshall L. Freimer, Ronald A. Howard · Journal of the American Statistical Association | Jun 1961 | 3,454 |
| 15 | Evolving Neural Networks through Augmenting Topologies Kenneth Owen Stanley, Risto P Miikkulainen · Evolutionary Computation | Jun 2002 | 3,317 |
| 16 | A survey of robot learning from demonstration Brenna Argall, Sonia Chernova, Manuela Veloso et al. · Robotics and Autonomous Systems | 3 Dec 2008 | 3,299 |
| 17 | Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning Richard S. Sutton, Doina Precup, Satinder Pal Singh · Artificial Intelligence | Aug 1999 | 3,216 |
| 18 | Reinforcement learning in robotics: A survey Jens Kober, James Andrew Bagnell, Jan Peters · The International Journal of Robotics Research | 23 Aug 2013 | 3,175 |
| 19 | Trust Region Policy Optimization John Schulman, Sergey Levine, Philipp Moritz et al. · arXiv (Cornell University) | 19 Feb 2015 | 3,119 |
| 20 | A Tutorial on the Cross-Entropy Method Pieter-Tjerk de Boer, Dirk P. Kroese, Shie Mannor et al. · Annals of Operations Research | Feb 2005 | 3,111 |
| 21 | A Survey of Monte Carlo Tree Search Methods Cameron Browne, Edward J. Powley, Daniel Whitehouse et al. · IEEE Transactions on Computational Intelligence and AI in Games | 3 Feb 2012 | 3,032 |
| 22 | Bandit Based Monte-Carlo Planning Levente Kocsis, Csaba Szepesvári · Lecture notes in computer science | 2006 | 2,928 |
| 23 | Markov games as a framework for multi-agent reinforcement learning Michael L. Littman · Elsevier eBooks | 1994 | 2,722 |
| 24 | Regularized Evolution for Image Classifier Architecture Search Esteban Real, Alok Aggarwal, Yanping Huang et al. · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) | 17 Jul 2019 | 2,713 |
| 25 | A contextual-bandit approach to personalized news article recommendation Lihong Li, Wei Chu, John C. Langford et al. | 26 Apr 2010 | 2,613 |
| 26 | SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient Lantao Yu, Weinan Zhang, Jun Wang et al. · Proceedings of the AAAI Conference on Artificial Intelligence | 13 Feb 2017 | 2,321 |
| 27 | A Comprehensive Survey of Multiagent Reinforcement Learning Lucian Buşoniu, Robert Babuška, Bart De Schutter · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) | 27 Feb 2008 | 2,277 |
| 28 | Sigmoid-weighted linear units for neural network function approximation in reinforcement learning Stefan Elfwing, Eiji Uchibe, Kenji Doya · Neural Networks | 11 Jan 2018 | 2,075 |
| 29 | Maximum Entropy Inverse Reinforcement Learning Brian D. Ziebart, Andrew L. Maas, James Andrew Bagnell et al. | 2018 | 2,046 |
| 30 | Prioritized Experience Replay Tom Schaul, John Quan, Ioannis Antonoglou et al. · arXiv (Cornell University) | 18 Nov 2015 | 2,041 |
| 31 | Markov Decision Processes: Discrete Stochastic Dynamic Programming Laurence A. Baxter, Martin L. Puterman · Technometrics | Aug 1995 | 1,898 |
| 32 | Generative Agents: Interactive Simulacra of Human Behavior Joon Sung Park, Joseph O’Brien, Carrie Jun Cai et al. | 20 Oct 2023 | 1,861 |
| 33 | Actor-critic algorithms Vijay R. Konda, John N. Tsitsiklis | 2002 | 1,816 |
| 34 | Dueling Network Architectures for Deep Reinforcement Learning Ziyu Wang, Tom Schaul, Matteo Hessel et al. · arXiv (Cornell University) | 20 Nov 2015 | 1,809 |
| 35 | Rainbow: Combining Improvements in Deep Reinforcement Learning Matteo Hessel, Joseph Modayil, Hado van Hasselt et al. · Proceedings of the AAAI Conference on Artificial Intelligence | 29 Apr 2018 | 1,798 |
| 36 | Counterfactual Multi-Agent Policy Gradients Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras et al. · Proceedings of the AAAI Conference on Artificial Intelligence | 29 Apr 2018 | 1,751 |
| 37 | High-Dimensional Continuous Control Using Generalized Advantage Estimation John Schulman, Philipp Moritz, Sergey Levine et al. · arXiv (Cornell University) | 8 Jun 2015 | 1,745 |
| 38 | Deterministic Policy Gradient Algorithms David Lawrence Silver, Guy Lever, Nicolas Heess et al. · HAL (Le Centre pour la Communication Scientifique Directe) | 2014 | 1,735 |
| 39 | End-to-end training of deep visuomotor policies Sergey Levine, Chelsea Finn, Trevor J. Darrell et al. · Journal of Machine Learning Research | 2016 | 1,699 |
| 40 | Self-improving reactive agents based on reinforcement learning, planning and teaching Long-Ji Lin · Machine Learning | May 1992 | 1,655 |
| 41 | Learning dexterous in-hand manipulation OpenAI Marcin Andrychowicz, Bowen Baker, Maciek Chociej et al. · The International Journal of Robotics Research | 18 Nov 2019 | 1,642 |
| 42 | Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors Auke Jan Ijspeert, Jun Nakanishi, Heiko Hoffmann et al. · Neural Computation | 13 Nov 2012 | 1,625 |
| 43 | A survey on large language model based autonomous agents Lei Wang, Chen Ma, Xueyang Feng et al. · Frontiers of Computer Science | 22 Mar 2024 | 1,624 |
| 44 | Deep Reinforcement Learning That Matters Peter B. Henderson, Riashat Islam, Philip Bachman et al. · Proceedings of the AAAI Conference on Artificial Intelligence | 29 Apr 2018 | 1,590 |
| 45 | Reinforcement learning and adaptive dynamic programming for feedback control Frank L. Lewis, Draguna Vrabie · IEEE Circuits and Systems Magazine | 2009 | 1,574 |
| 46 | Transfer Learning for Reinforcement Learning Domains: A Survey Matthew Edmund Taylor, Peter Stone · Journal of Machine Learning Research | Dec 2009 | 1,562 |
| 47 | Target-driven visual navigation in indoor scenes using deep reinforcement learning Yuke Zhu, Roozbeh Mottaghi, Eric Kolve et al. | May 2017 | 1,526 |
| 48 | Learning agile and dynamic motor skills for legged robots Jemin Hwangbo, Joonho Lee, Alexey Dosovitskiy et al. · Science Robotics | 16 Jan 2019 | 1,509 |
| 49 | Temporal difference learning and TD-Gammon Gerald Tesauro · Communications of the ACM | Mar 1995 | 1,496 |
| 50 | Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates Shixiang Gu, Ethan Holly, Timothy Lillicrap et al. | May 2017 | 1,474 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.