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ORCIDADPRL 2014: Orlando, FL, USA
- 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, ADPRL 2014, Orlando, FL, USA, December 9-12, 2014. IEEE 2014, ISBN 978-1-4799-4553-5
- Wei Sun, Evangelos A. Theodorou, Panagiotis Tsiotras:
Continuous-time differential dynamic programming with terminal constraints. 1-6 - Oktay Arslan, Evangelos A. Theodorou, Panagiotis Tsiotras:
Information-theoretic stochastic optimal control via incremental sampling-based algorithms. 1-8 - Timothé Collet, Olivier Pietquin
:
Active learning for classification: An optimistic approach. 1-8 - Xiangnan Zhong, Zhen Ni, Yufei Tang, Haibo He:
Data-driven partially observable dynamic processes using adaptive dynamic programming. 1-8 - Eugene A. Feinberg, Pavlo O. Kasyanov, Michael Z. Zgurovsky:
Convergence of value iterations for total-cost MDPs and POMDPs with general state and action sets. 1-8 - Lei Liu, Zhanshan Wang, Zhengwei Shen:
Neural-network-based adaptive dynamic surface control for MIMO systems with unknown hysteresis. 1-6 - Balázs Csanád Csáji, András Kovács, József Váncza:
Adaptive aggregated predictions for renewable energy systems. 1-8 - Ali Heydari:
Theoretical analysis of a reinforcement learning based switching scheme. 1-6 - Xiaohong Cui, Yanhong Luo, Huaguang Zhang:
An adaptive dynamic programming algorithm to solve optimal control of uncertain nonlinear systems. 1-6 - Simon Haykin, Ashkan Amiri, Mehdi Fatemi:
Cognitive control in cognitive dynamic systems: A new way of thinking inspired by the brain. 1-7 - Daniel L. Elliott, Charles Anderson:
Using supervised training signals of observable state dynamics to speed-up and improve reinforcement learning. 1-8 - Yang Liu, Yanhong Luo, Huaguang Zhang:
Adaptive dynamic programming for discrete-time LQR optimal tracking control problems with unknown dynamics. 1-6 - Taishi Fujita, Toshimitsu Ushio:
Reinforcement learning-based optimal control considering L computation time delay of linear discrete-time systems. 1-6 - Hadrien Glaude, Olivier Pietquin, Cyrille Enderli:
Subspace identification for predictive state representation by nuclear norm minimization. 1-8 - Deon Garrett, Jordi Bieger, Kristinn R. Thórisson:
Tunable and generic problem instance generation for multi-objective reinforcement learning. 1-8 - Martin W. Allen, David Hahn, Douglas C. MacFarland:
Heuristics for multiagent reinforcement learning in decentralized decision problems. 1-8 - Madalina M. Drugan, Ann Nowé, Bernard Manderick:
Pareto Upper Confidence Bounds algorithms: An empirical study. 1-8 - Minwoo Lee, Charles W. Anderson:
Convergent reinforcement learning control with neural networks and continuous action search. 1-8 - Yuhai Hu, Boris Defourny:
Near-optimality bounds for greedy periodic policies with application to grid-level storage. 1-8 - Marco A. Wiering, Maikel Withagen, Madalina M. Drugan:
Model-based multi-objective reinforcement learning. 1-6 - Hao Xu, Sarangapani Jagannathan:
Model-free Q-learning over finite horizon for uncertain linear continuous-time systems. 1-6 - Avimanyu Sahoo, Hao Xu, Sarangapani Jagannathan:
Event-based optimal regulator design for nonlinear networked control systems. 1-8 - Li-Bing Wu, Dan Ye, Xin-Gang Zhao:
Adaptive fault identification for a class of nonlinear dynamic systems. 1-6 - Hengshuai Yao, Csaba Szepesvári, Bernardo Ávila Pires, Xinhua Zhang:
Pseudo-MDPs and factored linear action models. 1-9 - Qinglai Wei, Derong Liu, Guang Shi, Yu Liu, Qiang Guan:
Optimal self-learning battery control in smart residential grids by iterative Q-learning algorithm. 1-7 - Simone Parisi, Matteo Pirotta, Nicola Smacchia, Luca Bascetta, Marcello Restelli:
Policy gradient approaches for multi-objective sequential decision making: A comparison. 1-8 - Sumit Kumar Jha, Shubhendu Bhasin:
On-policy Q-learning for adaptive optimal control. 1-6 - Seyed Reza Ahmadzadeh, Petar Kormushev, Darwin G. Caldwell:
Multi-objective reinforcement learning for AUV thruster failure recovery. 1-8 - Vincent François-Lavet, Raphaël Fonteneau, Damien Ernst:
Using approximate dynamic programming for estimating the revenues of a hydrogen-based high-capacity storage device. 1-8 - Xiaofeng Lin, Qiang Ding, Weikai Kong, Chunning Song, Qingbao Huang:
Adaptive dynamic programming-based optimal tracking control for nonlinear systems using general value iteration. 1-6 - Haci Mehmet Guzey, Hao Xu, Sarangapani Jagannathan:
Neural network-based adaptive optimal consensus control of leaderless networked mobile robots. 1-6 - Lucian Busoniu, Rémi Munos, Elod Páll:
An analysis of optimistic, best-first search for minimax sequential decision making. 1-8 - Dominik Meyer, Rémy Degenne, Ahmed Omrane, Hao Shen:
Accelerated gradient temporal difference learning algorithms. 1-8 - Daniel R. Jiang, Thuy V. Pham, Warren B. Powell, Daniel F. Salas, Warren R. Scott:
A comparison of approximate dynamic programming techniques on benchmark energy storage problems: Does anything work? 1-8 - Yanhong Luo, Geyang Xiao:
ADP-based optimal control for a class of nonlinear discrete-time systems with inequality constraints. 1-5 - Regina Padmanabhan, Nader Meskin, Wassim M. Haddad:
Closed-loop control of anesthesia and mean arterial pressure using reinforcement learning. 1-8 - Abhijit Gosavi, Sajal K. Das, Susan L. Murray:
Beyond exponential utility functions: A variance-adjusted approach for risk-averse reinforcement learning. 1-8 - Yunpeng Pan, Evangelos A. Theodorou:
Nonparametric infinite horizon Kullback-Leibler stochastic control. 1-8 - Saba Q. Yahyaa, Madalina M. Drugan, Bernard Manderick:
Annealing-pareto multi-objective multi-armed bandit algorithm. 1-8 - Ahmad A. Al-Talabi, Howard M. Schwartz:
A two stage learning technique for dual learning in the pursuit-evasion differential game. 1-8 - Yuanheng Zhu, Dongbin Zhao:
A data-based online reinforcement learning algorithm with high-efficient exploration. 1-6 - Joschka Boedecker, Jost Tobias Springenberg, Jan Wülfing, Martin A. Riedmiller:
Approximate real-time optimal control based on sparse Gaussian process models. 1-8
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