ADPRL 2011 - ADPRL 2011 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning

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Category ADPRL 2011

Deadline: October 31, 2010 | Date: April 11, 2011-April 15, 2011

Venue/Country: Paris, France

Updated: 2010-08-02 13:25:21 (GMT+9)

Call For Papers - CFP

2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning

Adaptive (or Approximate) dynamic programming (ADP) is a general and effective approach for solving optimal control problems by adapting to uncertain environments over time. ADP optimizes a user-defined cost function with respect to an adaptive control law, conditioned on prior knowledge of the system, and its state, in the presence of system uncertainties. A numerical search over the present value of the control minimizes a nonlinear cost function forward-in-time providing a basis for real-time, approximate optimal control. The ability to improve performance over time subject to new or unexplored objectives or dynamics has made ADP an attractive approach in a number of application domains including optimal control and estimation, operation research, and computational intelligence. ADP is viewed as a form of reinforcement learning based on an actor-critic architecture that optimizes a user-prescribed value online and obtains the resulting optimal control policy.

Reinforcement learning (RL) algorithms learn to optimize an agent by letting it interact with an environment and learn from its received feedback. The goal of the agent is to optimize its accumulated reward over time, and for this it estimates value functions that predict its future reward intake when executing a particular policy. Reinforcement learning techniques can be combined with many different function approximators and do not assume any a priori knowledge about the environment. An important aspect in RL is that an agent has to explore parts of the environment it does not know well, while at the same time it has to exploit its knowledge to maximize its reward intake. RL techniques have already been applied successfully for many problems such as controlling robots, game playing, elevator control, network routing, and traffic light optimization.

Topics

The symposium topics include, but are not limited to:

Convergence and performance bounds of ADP

Complexity issues in RL and ADP

Statistical learning and RL, PAC bounds for RL

Monte-Carlo and quasi Monte-Carlo methods

Direct policy search, actor-critic methods

Parsimoneous function representation

Adaptive feature discovery

Learning rules and architectures for RL

Sensitivity analysis for policy gradient estimation

Neuroscience and biologically inspired control

Partially observable Markov decision processes

Distributed intelligent systems

Multi-agent RL systems

Multi-level multi-objective optimization for ADPRL

Kernel methods and value function representation

Applications of ADP and RL

Symposium Chair

Jagannathan Sarangapani, Missouri University of Science and Technology, USA

Symposium Co-Chairs

Huaguang Zhang, Northeastern University, China

Marco Wiering, University of Groningen, Netherlands

Program Committee

Charles W. Anderson, Colorado State University, USA

S. N. Balakrishnan, Missouri University of Science and Technology, USA

Tamer Basar, University of Illinois, USA

Dimitri P. Bertsekas, Massachusetts Institute of Technology, USA

Mingcong Deng, Okayama University, Japan

Hai-Bin Duan, Beihang University, China

El-Sayed El-Alfy, King Fahd University of Petroleum and Minerals, Saudi Arabia

Xiao Hu, GE Global Research, USA

Derong Liu, University of Illinois Chicago, USA

Abhijit Gosavi, Missouri University of Science and Technology, USA

Zeng-Guang Hou, Chinese Academy of Sciences, China

Hossein Javaherian, General Motors, USA

George G. Lendaris, Portland State University, USA

Frank L. Lewis, University of Texas at Arlington, USA

Eduardo Morales, INAOE, Mexico

Remi Munos, INRIA Lille - Nord Europe, France

Kumpati S. Narendra, Yale University, USA

Hector D. Patino, Universidad Nacional de San Juan, Argentina

Jan Peters, Max Planck Institute for Biological Cybernetics, Germany

Warren Powell, Princeton University, USA

Philippe Preux, INRIA & CNRS (LIFL), France

Danil Prokhorov, Toyota Technical Center, USA

Jennie Si, Arizona State University, USA

L. Enrique Sucar, INAOE, Mexico

Csaba Szepesvari, University of Alberta, Canada

Antonios Tsourdos, Cranfield University (DCMT), UK

G. Kumar Venayagamoorthy, Missouri University of Science and Technology, USA

Draguna Vrabie, University of Texas at Arlington, USA

Paul Werbos, National Science Foundation, USA

Bernard Widrow, Stanford University, USA

Donald C. Wunsch, Missouri University of Science and Technology, USA

Gary G. Yen, Oklahoma State University, USA