Most cited papers in Adaptive Dynamic Programming Control
What are the most cited papers in Adaptive Dynamic Programming Control?
The most cited paper in Adaptive Dynamic Programming Control is "Continuous control with deep reinforcement learning" (Timothy Lillicrap, Jonathan J. Hunt, Alexander Pritzel et al., 2015), with 5,364 citations. Next come "Policy Gradient Methods for Reinforcement Learning with Function Approximation" (4,956) and "Robust Adaptive Control of Feedback Linearizable MIMO Nonlinear Systems With Prescribed Performance" (2,762).
Showing 50 of 100 papers. Every paper with more than 459 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Continuous control with deep reinforcement learning Timothy Lillicrap, Jonathan J. Hunt, Alexander Pritzel et al. · arXiv (Cornell University) | 9 Sep 2015 | 5,364 |
| 2 | Policy Gradient Methods for Reinforcement Learning with Function Approximation Richard S. Sutton, David McAllester, Satinder Pal Singh et al. | 29 Nov 1999 | 4,956 |
| 3 | Robust Adaptive Control of Feedback Linearizable MIMO Nonlinear Systems With Prescribed Performance Charalampos P. Bechlioulis, George A. Rovithakis · IEEE Transactions on Automatic Control | Oct 2008 | 2,762 |
| 4 | 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 |
| 5 | Systematic design of adaptive controllers for feedback linearizable systems I. Kanellakopoulos, P.V. Kokotović, A. Stephen Morse · IEEE Transactions on Automatic Control | Nov 1991 | 1,962 |
| 6 | Actor-critic algorithms Vijay R. Konda, John N. Tsitsiklis | 2002 | 1,816 |
| 7 | 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 |
| 8 | Deterministic Policy Gradient Algorithms David Lawrence Silver, Guy Lever, Nicolas Heess et al. · HAL (Le Centre pour la Communication Scientifique Directe) | 2014 | 1,735 |
| 9 | Online actor–critic algorithm to solve the continuous-time infinite horizon optimal control problem Kyriakos G. Vamvoudakis, Frank L. Lewis · Automatica | 18 Mar 2010 | 1,703 |
| 10 | Self-improving reactive agents based on reinforcement learning, planning and teaching Long-Ji Lin · Machine Learning | May 1992 | 1,655 |
| 11 | Reinforcement learning and adaptive dynamic programming for feedback control Frank L. Lewis, Draguna Vrabie · IEEE Circuits and Systems Magazine | 2009 | 1,574 |
| 12 | Stable adaptive neural control scheme for nonlinear systems Marios M. Polycarpou · IEEE Transactions on Automatic Control | Mar 1996 | 1,548 |
| 13 | A survey of consensus problems in multi-agent coordination Wei Ren, Randal W. Beard, Ella M. Atkins | 10 Aug 2005 | 1,360 |
| 14 | Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach Murad Abu-Khalaf, Frank L. Lewis · Automatica | 8 Mar 2005 | 1,353 |
| 15 | Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications Thanh Thi Nguyen, Ngoc Duy Nguyen, Saeid Nahavandi · IEEE Transactions on Cybernetics | 20 Mar 2020 | 1,301 |
| 16 | Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints Wei He, Yuhao Chen, Zhao Yin · IEEE Transactions on Cybernetics | 3 Apr 2015 | 1,279 |
| 17 | Neural Network-Based Adaptive Dynamic Surface Control for a Class of Uncertain Nonlinear Systems in Strict-Feedback Form Dan Wang, Jie Huang · IEEE Transactions on Neural Networks | 2005 | 1,266 |
| 18 | Adaptive critic designs Danil V. Prokhorov, Donald C. Wunsch · IEEE Transactions on Neural Networks | Sep 1997 | 1,196 |
| 19 | Reinforcement Learning and Feedback Control: Using Natural Decision Methods to Design Optimal Adaptive Controllers Frank L. Lewis, Draguna Vrabie, Kyriakos G. Vamvoudakis · IEEE Control Systems | 16 Nov 2012 | 1,112 |
| 20 | Adaptive Neural Control for Output Feedback Nonlinear Systems Using a Barrier Lyapunov Function Beibei Ren, Shuzhi Sam Ge, Keng Peng Tee et al. · IEEE Transactions on Neural Networks | 8 Jul 2010 | 1,087 |
| 21 | Discrete-Time Nonlinear HJB Solution Using Approximate Dynamic Programming: Convergence Proof A. Al-Tamimi, Frank L. Lewis, Murad Abu-Khalaf · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) | 24 Jul 2008 | 1,078 |
| 22 | Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics Yu Jiang, Zhong‐Ping Jiang · Automatica | 21 Jul 2012 | 1,076 |
| 23 | A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients I. Grondman, Lucian Buşoniu, Gabriel A. D. Lopes et al. · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) | Nov 2012 | 1,074 |
| 24 | Adaptive control with guaranteed transient and steady state tracking error bounds for strict feedback systems Charalampos P. Bechlioulis, George A. Rovithakis · Automatica | 18 Dec 2008 | 1,056 |
| 25 | Reinforcement Learning in Continuous Time and Space Kenji Doya · Neural Computation | 2000 | 995 |
| 26 | Adaptive optimal control for continuous-time linear systems based on policy iteration Draguna Vrabie, Octavian C. Pastravanu, Murad Abu-Khalaf et al. · Automatica | 28 Dec 2008 | 958 |
| 27 | Optimal and Autonomous Control Using Reinforcement Learning: A Survey Bahare Kiumarsi, Kyriakos G. Vamvoudakis, Hamidreza Modares et al. · IEEE Transactions on Neural Networks and Learning Systems | 7 Dec 2017 | 910 |
| 28 | Adaptive Dynamic Programming: An Introduction Fei–Yue Wang, Huaguang Zhang, Derong Liu · IEEE Computational Intelligence Magazine | 24 Apr 2009 | 906 |
| 29 | Cooperative Multi-agent Control Using Deep Reinforcement Learning Jayesh K. Gupta, Maxim Egorov, Mykel J. Kochenderfer · Lecture notes in computer science | 2017 | 904 |
| 30 | Barrier Lyapunov Functions-based adaptive control for a class of nonlinear pure-feedback systems with full state constraints Yan‐Jun Liu, Shaocheng Tong · Automatica | 7 Dec 2015 | 883 |
| 31 | Finite-time command filtered backstepping control for a class of nonlinear systems Jinpeng Yu, Peng Shi, Lin Hui Zhao · Automatica | 25 Mar 2018 | 878 |
| 32 | Tree-Based Batch Mode Reinforcement Learning Damien Ernst, Pierre Geurts, Louis A. Wehenkel | Dec 2005 | 861 |
| 33 | On the Convergence of Stochastic Iterative Dynamic Programming Algorithms Tommi Jaakkola, Michael I. Jordan, Satinder Pal Singh · Neural Computation | Nov 1994 | 807 |
| 34 | Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation Wei He, Yiting Dong, Changyin Sun · IEEE Transactions on Systems Man and Cybernetics Systems | 3 Jun 2015 | 795 |
| 35 | Stabilization of stochastic nonlinear systems driven by noise of unknown covariance Hua Deng, Miroslav Krstić, Ruth J. Williams · IEEE Transactions on Automatic Control | 2001 | 787 |
| 36 | Online learning control by association and reinforcement Jennie Si, Yu-tsung Wang · IEEE Transactions on Neural Networks | Mar 2001 | 780 |
| 37 | Policy Iteration Adaptive Dynamic Programming Algorithm for Discrete-Time Nonlinear Systems Derong Liu, Qinglai Wei · IEEE Transactions on Neural Networks and Learning Systems | 25 Sep 2013 | 758 |
| 38 | Design of Robust Adaptive Controllers for Nonlinear Systems with Dynamic Uncertainties Zhong‐Ping Jiang, Laurent Praly · Automatica | Jul 1998 | 734 |
| 39 | The Option-Critic Architecture Pierre‐Luc Bacon, Jean Harb, Doina Precup · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) | 13 Feb 2017 | 719 |
| 40 | Adaptive Neural Control of Nonlinear Time-Delay Systems With Unknown Virtual Control Coefficients Shuzhi Sam Ge, F. Hong, T.H. Lee · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) | Feb 2004 | 717 |
| 41 | Adaptive Dynamic Programming for Control: A Survey and Recent Advances Derong Liu, Shan Xue, Bo Zhao et al. · IEEE Transactions on Systems Man and Cybernetics Systems | 24 Dec 2020 | 710 |
| 42 | Optimal tracking control of nonlinear partially-unknown constrained-input systems using integral reinforcement learning Hamidreza Modares, Frank L. Lewis · Automatica | 26 May 2014 | 700 |
| 43 | Adaptive dynamic programming J. Murray, Chadwick J. Cox, G.G. Lendaris et al. · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) | May 2002 | 683 |
| 44 | An Overview of Recent Advances in Fixed-Time Cooperative Control of Multiagent Systems Zongyu Zuo, Qing‐Long Han, Boda Ning et al. · IEEE Transactions on Industrial Informatics | 22 Mar 2018 | 666 |
| 45 | Observer-Based Neuro-Adaptive Optimized Control of Strict-Feedback Nonlinear Systems With State Constraints Yongming Li, Yan‐Jun Liu, Shaocheng Tong · IEEE Transactions on Neural Networks and Learning Systems | 26 Jan 2021 | 661 |
| 46 | Adaptive control-based Barrier Lyapunov Functions for a class of stochastic nonlinear systems with full state constraints Yan‐Jun Liu, Shumin Lu, Shaocheng Tong et al. · Automatica | 19 Oct 2017 | 659 |
| 47 | Neural-Network-Based Near-Optimal Control for a Class of Discrete-Time Affine Nonlinear Systems With Control Constraints Huaguang Zhang, Yanhong Luo, Derong Liu · IEEE Transactions on Neural Networks | 7 Aug 2009 | 650 |
| 48 | Data-Driven Robust Approximate Optimal Tracking Control for Unknown General Nonlinear Systems Using Adaptive Dynamic Programming Method Huaguang Zhang, Lili Cui, Xin Zhang et al. · IEEE Transactions on Neural Networks | 13 Oct 2011 | 647 |
| 49 | Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning Wei He, Yiting Dong · IEEE Transactions on Neural Networks and Learning Systems | Mar 2017 | 645 |
| 50 | A novel actor–critic–identifier architecture for approximate optimal control of uncertain nonlinear systems Shubhendu Bhasin, Rushikesh Kamalapurkar, Margaret L. Johnson et al. · Automatica | 23 Oct 2012 | 645 |
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.