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IEEE CIS > Technical Activities
Adaptive Dynamic Programming and Reinforcement Learning TC
(1) Officers
 | Derong Liu, Chair (2009) Department of Electrical and Computer Engineering (M/C 154) University of Illinois at Chicago 851 S. Morgan Street Chicago, IL 60607-7053, USA phone: (+1 312) 355 4475 fax: (+1 312) 996 6465 email: dliu .a_t. ieee.org www: liu.ece.uic.edu |
 | Marco Wiering, Vice Chair Department of Information and Computing Sciences University Utrecht Padualaan 14, De Uithof Utrecht 3584CH, Netherlands phone: (+31) 30 2539209 fax: (+31) 30 2513791 email: marco .a_t. cs.uu.nl www: people.cs.uu.nl/marco/ |
(2) Members
 | Hamid R. Berenji Intelligent Inference Systems 333 W. Maude Ave, Suite 105 Sunnyvale, CA 94085, USA phone: (+1 408) 730 8345 fax: (+1 408) 730 8550 email: berenji .a_t. sonic.net |
 | Derong Liu Department of Electrical and Computer Engineering (M/C 154) University of Illinois at Chicago 851 S. Morgan Street Chicago, IL 60607-7053, USA phone: (+1 312) 355 4475 fax: (+1 312) 996 6465 email: dliu .a_t. ieee.org www: liu.ece.uic.edu |
 | Evangelia Micheli-Tzanakou Department of Biomedical Engineering Rutgers University 617 Bowser Road Piscataway, NJ 08855-0909, USA phone: (+1 732) 445 2037 fax: (+1 732) 445 3753 email: etzanako .a_t. rci.rutgers.edu |
 | Marco Wiering Department of Information and Computing Sciences University Utrecht Padualaan 14, De Uithof Utrecht 3584CH, Netherlands phone: (+31) 30 2539209 fax: (+31) 30 2513791 email: marco .a_t. cs.uu.nl www: people.cs.uu.nl/marco/ |
(3) Task Forces
3.1 Complexity issues in ADP and RL
Scope: Numerical complexity of solving ADP and RL problems: amount of numerical resources required to obtain near-optimal performances. Sample complexity for RL: number of calls to the generative model (or the real system) required to build a near-optimal approximations.
3.2 Important applications of ADP and RL
Analysis of power grid cascades and other cascades in man-made systems, analysis of evolution of epidemics, evolution of the stock market, optimal trading of commodities. There are also a large numbers of applications in economics, management science and other areas, and often they are less abstract and theoretical and tries to make actual, measurable, concrete contributions to improve decision making by firms, governments and other organizations.
3.3 Representation in ADP and RL
In ADP and RL, we need a function approximator to represent the learned function, either the value, or the policy, or a model of the dynamics. Tools used for such approximation includes neural networks and many others. There are also issues on how to represent a state in order to achieve the best learning curve. Many issues are intertwined here, ranging from fundamental issues, to algorithmic ones, and practical ones.
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