ADPRL

Keyphrases

Publications

2014

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

2013