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ORCIDADPRL 2011: Paris, France
- 2011 IEEE Symposium on Adaptive Dynamic Programming And Reinforcement Learning, ADPRL 2011, Paris, France, April 12-14, 2011. IEEE 2011, ISBN 978-1-4244-9888-8
Keynote
- George G. Lendaris:
Higher-level application of Adaptive Dynamic Programming/Reinforcement Learning - a next phase for controls and system identification?
Representations in Reinforcement Learning
- Lucian Busoniu
, Damien Ernst, Bart De Schutter, Robert Babuska:
Approximate reinforcement learning: An overview. 1-8 - Matthieu Geist, Olivier Pietquin:
Parametric value function approximation: A unified view. 9-16 - Shivaram Kalyanakrishnan, Peter Stone:
On learning with imperfect representations. 17-24
Active Reinforcement Learning
- Oliver Kroemer, Jan Peters:
Active exploration for robot parameter selection in episodic reinforcement learning. 25-31 - Kun Deng, Joelle Pineau, Susan A. Murphy:
Active learning for personalizing treatment. 32-39 - Raphael Fonteneau, Susan A. Murphy, Louis Wehenkel, Damien Ernst:
Active exploration by searching for experiments that falsify the computed control policy. 40-47 - Lucian Busoniu, Rémi Munos, Bart De Schutter, Robert Babuska:
Optimistic planning for sparsely stochastic systems. 48-55 - Chunming Liu, Xin Xu, Haiyun Hu, Bin Dai:
Adaptive sample collection using active learning for kernel-based approximate policy iteration. 56-61 - Andrea Castelletti, Stefano Galelli, Marcello Restelli, Rodolfo Soncini-Sessa:
Tree-based variable selection for dimensionality reduction of large-scale control systems. 62-69
Reinforcement Learning I
- Yuval Tassa, Emanuel Todorov:
High-order local dynamic programming. 70-75 - Francisco Javier García-Polo, Fernando Fernández Rebollo:
Safe reinforcement learning in high-risk tasks through policy improvement. 76-83 - Alexander Hans, Siegmund Duell, Steffen Udluft:
Agent self-assessment: Determining policy quality without execution. 84-90 - Marco A. Wiering, Hado van Hasselt, Auke-Dirk Pietersma, Lambert Schomaker:
Reinforcement learning algorithms for solving classification problems. 91-96 - Jason Pazis, Michail G. Lagoudakis:
Reinforcement learning in multidimensional continuous action spaces. 97-104 - Sander G. van Dijk, Daniel Polani:
Grounding subgoals in information transitions. 105-111
Reinforcement Learning II
- Ioannis Rexakis, Michail G. Lagoudakis:
Directed exploration of policy space using support vector classifiers. 112-119 - Shimon Whiteson, Brian Tanner, Matthew E. Taylor, Peter Stone:
Protecting against evaluation overfitting in empirical reinforcement learning. 120-127 - Ashley Edwards, William M. Pottenger:
Higher order Q-Learning. 128-134
Recent Advances in Reinforcement Learning
- Ilya O. Ryzhov, Warren B. Powell:
Bayesian active learning with basis functions. 143-150 - Mohsen Davarynejad, Jelmer van Ast, Jos L. M. Vrancken, Jan van den Berg:
Evolutionary value function approximation. 151-155 - Andreas Witsch, Roland Reichle, Kurt Geihs, Sascha Lange, Martin A. Riedmiller:
Enhancing the episodic natural actor-critic algorithm by a regularisation term to stabilize learning of control structures. 156-163 - Vishnuteja Nanduri:
Application of reinforcement learning-based algorithms in CO2 allowance and electricity markets. 164-169 - Abhijit Gosavi, Susan L. Murray, Jiaqiao Hu:
Model-building semi-Markov adaptive critics. 170-175 - Matthew J. Reindorp, Michael C. Fu:
Dynamic lead time promising. 176-183
Approximate Dynamic Programming for Optimal Control of Nonlinear Systems
- Ruizhuo Song, Huaguang Zhang:
N-step optimal time-invariant trajectory tracking control for a class of nonlinear systems. 184-189 - Lili Cui, Huaguang Zhang, Xin Zhang, Yanhong Luo:
Data-based adaptive critic design for discrete-time zero-sum games using output feedback. 190-195 - Xin Zhang, Huaguang Zhang, Lili Cui, Yanhong Luo:
Global optimal strategies of a class of finite-horizon continuous-time nonaffine nonlinear zero-sum game using a new iteration algorithm. 196-201 - Daniel A. Braun, Pedro A. Ortega, Evangelos A. Theodorou, Stefan Schaal:
Path integral control and bounded rationality. 202-209 - Jian Fu, Haibo He, Zhen Ni:
Adaptive dynamic programming with balanced weights seeking strategy. 210-217 - Mingyuan Zhong, Emanuel Todorov:
Moving least-squares approximations for linearly-solvable MDP. 218-225
Approximate Dynamic Programming for Feedback Control; Multiagent Systems and Multiplayer Games
- Travis Dierks, Bryan Brenner, Sarangapani Jagannathan:
Near optimal control of mobile robot formations. 234-241 - Derong Liu, Ding Wang, Dongbin Zhao:
Adaptive dynamic programming for optimal control of unknown nonlinear discrete-time systems. 242-249 - Kyriakos G. Vamvoudakis, Draguna L. Vrabie, Frank L. Lewis:
Online adaptive learning of optimal control solutions using integral reinforcement learning. 250-257 - Hassan Zargarzadeh, Sarangapani Jagannathan, James A. Drallmeier:
Online near optimal control of unknown nonaffine systems with application to HCCI engines. 258-263 - Petru Emanuel Stingu, Frank L. Lewis:
An approximate Dynamic Programming based controller for an underactuated 6DoF quadrotor. 271-278
Applications of Reinforcement Learning
- Thomas Gabel, Christian Lutz, Martin A. Riedmiller:
Improved neural fitted Q iteration applied to a novel computer gaming and learning benchmark. 279-286 - Martino Migliavacca, Alessio Pecorino, Matteo Pirotta, Marcello Restelli, Andrea Bonarini:
Fitted policy search. 287-294 - El-Sayed M. El-Alfy:
A reinforcement learning approach for sequential mastery testing. 295-301
Optimal Control and Applications
- Suman Chakravorty, Richard Scott Erwin:
Information space receding horizon control. 302-309 - John W. Roberts, Ian R. Manchester, Russ Tedrake:
Feedback controller parameterizations for Reinforcement Learning. 310-317 - Dongbin Zhao, Zhaohui Hu:
Supervised adaptive dynamic programming based adaptive cruise control. 318-323 - Abdeslem Boukhtouta, Jean Berger, Warren B. Powell, Abraham P. George:
An adaptive-learning framework for semi-cooperative multi-agent coordination. 324-331 - Hisashi Handa:
Structure search of probabilistic models and data correction for EDA-RL. 332-337 - Alex Simpkins, Emanuel Todorov:
Complex object manipulation with hierarchical optimal control. 338-345
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