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Publication search results
found 29 matches
- 2013
- Mostafa D. Awheda, Howard M. Schwartz:
Exponential moving average Q-learning algorithm. ADPRL 2013: 31-38 - Luuk Bom, Ruud Henken, Marco A. Wiering:
Reinforcement learning to train Ms. Pac-Man using higher-order action-relative inputs. ADPRL 2013: 156-163 - Lucian Busoniu, Alexander Daniels, Rémi Munos, Robert Babuska:
Optimistic planning for continuous-action deterministic systems. ADPRL 2013: 69-76 - Yifan Cai, Simon X. Yang, Xin Xu:
A combined hierarchical reinforcement learning based approach for multi-robot cooperative target searching in complex unknown environments. ADPRL 2013: 52-59 - Raphaël Fonteneau, Lucian Busoniu, Rémi Munos:
Optimistic planning for belief-augmented Markov Decision Processes. ADPRL 2013: 77-84 - Qi-ming Fu, Quan Liu, Fei Xiao, Guixin Chen:
The second order temporal difference error for Sarsa(λ). ADPRL 2013: 60-68 - Hisashi Handa:
On the coordination system for the dimensionality-reduced inputs of mario. ADPRL 2013: 170-176 - Yujiao Huang, Huaguang Zhang, Dongsheng Yang:
Local stability analysis of high-order recurrent neural networks with multi-step piecewise linear activation functions. ADPRL 2013: 1-5 - Tobias Jung, Damien Ernst, Francis Maes:
Optimized look-ahead trees: Extensions to large and continuous action spaces. ADPRL 2013: 85-92 - 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. ADPRL 2013: 116-123 - Donghun Lee, Boris Defourny, Warren B. Powell:
Bias-corrected Q-learning to control max-operator bias in Q-learning. ADPRL 2013: 93-99 - 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. ADPRL 2013: 177-182 - Robert Lowe, Tom Ziemke:
Exploring the relationship of reward and punishment in reinforcement learning. ADPRL 2013: 140-147 - Xiong Luo, Jennie Si, Yuchao Zhou:
An integrated design for intensified direct heuristic dynamic programming. ADPRL 2013: 183-190 - Kristof Van Moffaert, Madalina M. Drugan, Ann Nowé:
Scalarized multi-objective reinforcement learning: Novel design techniques. ADPRL 2013: 191-199 - Zhen Ni, Xiao Fang, Haibo He, Dongbin Zhao, Xin Xu:
Real-time tracking on adaptive critic design with uniformly ultimately bounded condition. ADPRL 2013: 39-46 - Chunbin Qin, Huaguang Zhang, Yanhong Luo:
Adaptive optimal control for nonlinear discrete-time systems. ADPRL 2013: 13-18 - Michiel van der Ree, Marco A. Wiering:
Reinforcement learning in the game of Othello: Learning against a fixed opponent and learning from self-play. ADPRL 2013: 108-115 - Sachiko Soga, Ichiro Kobayashi:
A study on the efficiency of learning a robot controller in various environments. ADPRL 2013: 164-169 - Ruizhuo Song, Wendong Xiao, Yanhong Luo:
Optimal control for a class of nonlinear system with controller constraints based on finite-approximation-errors ADP algorithm. ADPRL 2013: 19-23 - Teck-Hou Teng, Ah-Hwee Tan:
Delayed insertion and rule effect moderation of domain knowledge for reinforcement learning. ADPRL 2013: 132-139 - Evangelos A. Theodorou, Jiri Najemnik, Emanuel Todorov:
Free energy based policy gradients. ADPRL 2013: 124-131 - Zhanshan Wang, Fufei Chu, Hongjing Liang, Huaguang Zhang:
Fault accommodation for complete synchronization of complex neural networks. ADPRL 2013: 200-205 - Jian Wang, Zhenhua Huang, Xin Xu:
A novel approach for constructing basis functions in approximate dynamic programming for feedback control. ADPRL 2013: 47-51 - Hao Xu, Sarangapani Jagannathan:
Finite horizon stochastic optimal control of uncertain linear networked control system. ADPRL 2013: 24-30 - Toshiyuki Yasuda, Nanami Wada, Kazuhiro Ohkura, Yoshiyuki Matsumura:
Analyzing collective behavior in evolutionary swarm robotic systems based on an ethological approach. ADPRL 2013: 148-155 - Qiming Zhao, Hao Xu, Sarangapani Jagannathan:
Finite-horizon optimal control design for uncertain linear discrete-time systems. ADPRL 2013: 6-12 - Mingyuan Zhong, M. Johnson, Yuval Tassa, Tom Erez, Emo Todorov:
Value function approximation and model predictive control. ADPRL 2013: 100-107 - 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 [contents]
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