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Semantic ScholarADPRL 2013: Singapore
Proceedings of the 2013 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, ADPRL 2013, IEEE Symposium Series on Computational Intelligence (SSCI), 16-19 April 2013, Singapore. IEEE 2013, ISBN 978-1-4673-5925-2- Yujiao Huang, Huaguang Zhang, Dongsheng Yang:
Local stability analysis of high-order recurrent neural networks with multi-step piecewise linear activation functions. 1-5 - Qiming Zhao, Hao Xu, Sarangapani Jagannathan:
Finite-horizon optimal control design for uncertain linear discrete-time systems. 6-12 - Chunbin Qin, Huaguang Zhang, Yanhong Luo:
Adaptive optimal control for nonlinear discrete-time systems. 13-18 - Ruizhuo Song, Wendong Xiao, Yanhong Luo:
Optimal control for a class of nonlinear system with controller constraints based on finite-approximation-errors ADP algorithm. 19-23 - Hao Xu, Sarangapani Jagannathan:
Finite horizon stochastic optimal control of uncertain linear networked control system. 24-30 - Zhen Ni, Xiao Fang, Haibo He, Dongbin Zhao, Xin Xu:
Real-time tracking on adaptive critic design with uniformly ultimately bounded condition. 39-46 - Jian Wang, Zhenhua Huang, Xin Xu:
A novel approach for constructing basis functions in approximate dynamic programming for feedback control. 47-51 - Yifan Cai, Simon X. Yang, Xin Xu:
A combined hierarchical reinforcement learning based approach for multi-robot cooperative target searching in complex unknown environments. 52-59 - Qi-ming Fu, Quan Liu, Fei Xiao, Guixin Chen:
The second order temporal difference error for Sarsa(λ). 60-68 - Lucian Busoniu, Alexander Daniels, Rémi Munos, Robert Babuska:
Optimistic planning for continuous-action deterministic systems. 69-76 - Raphaël Fonteneau, Lucian Busoniu, Rémi Munos:
Optimistic planning for belief-augmented Markov Decision Processes. 77-84 - Tobias Jung, Damien Ernst, Francis Maes:
Optimized look-ahead trees: Extensions to large and continuous action spaces. 85-92 - Donghun Lee, Boris Defourny, Warren B. Powell:
Bias-corrected Q-learning to control max-operator bias in Q-learning. 93-99 - Mingyuan Zhong, M. Johnson, Yuval Tassa, Tom Erez, Emo Todorov:
Value function approximation and model predictive control. 100-107 - M. van der Ree, Marco Wiering:
Reinforcement learning in the game of Othello: Learning against a fixed opponent and learning from self-play. 108-115 - A. Y. F. Lau, Dipti Srinivasan, Thomas Reindl:
A reinforcement learning algorithm developed to model GenCo strategic bidding behavior in multidimensional and continuous state and action spaces. 116-123 - Teck-Hou Teng, Ah-Hwee Tan:
Delayed insertion and rule effect moderation of domain knowledge for reinforcement learning. 132-139 - Robert Lowe, Tom Ziemke:
Exploring the relationship of reward and punishment in reinforcement learning. 140-147 - Toshiyuki Yasuda, Nanami Wada, Kazuhiro Ohkura, Yoshiyuki Matsumura:
Analyzing collective behavior in evolutionary swarm robotic systems based on an ethological approach. 148-155 - Luuk Bom, Ruud Henken, Marco Wiering:
Reinforcement learning to train Ms. Pac-Man using higher-order action-relative inputs. 156-163 - Sachiko Soga, Ichiro Kobayashi:
A study on the efficiency of learning a robot controller in various environments. 164-169 - Xiaofeng Lin, Nuyun Cao, Yuzhang Lin:
Optimal control for a class of nonlinear systems with state delay based on Adaptive Dynamic Programming with ε-error bound. 177-182 - Xiong Luo, Jennie Si, Yuchao Zhou:
An integrated design for intensified direct heuristic dynamic programming. 183-190 - Kristof Van Moffaert, Madalina M. Drugan, Ann Nowé:
Scalarized multi-objective reinforcement learning: Novel design techniques. 191-199 - Zhanshan Wang, Fufei Chu, Hongjing Liang, Huaguang Zhang:
Fault accommodation for complete synchronization of complex neural networks. 200-205
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