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IEEE CIS > Technical Activities
Adaptive Dynamic Programming and Reinforcement Learning TC
(1) Officers
 | Marco Wiering, Chair (2010)
Department of Artificial Intelligence
University of Groningen
Padualaan 14, De Uithof
Groningen, Nijenborgh 9 9700AK, Netherlands
phone: +31(0)50-3636956
fax: +31(0)50-3636687
email: m.a.wiering .a_t. rug.nl
www: www.ai.rug.nl/~mwiering |
(2) Members
 | Marcus Hutter
Research School of Information Sciences and Engineering
Australian National University
Corner of North and Daley Road
Canberra ACT 0200, Australia
phone: +61(0)2 612 51605
fax: +61(0)2 612 58651
email: marcus.hutter .a_t. anu.edu.au
www: www.hutter1.net |
 | Jagannathan Sarangapani
Electrical and Computer Engineering
Missouri university of Science and Technology
1870 Miner Circle
Rolla, MO 65409, USA
phone: (573)341-6775
fax: (573)341-4532
email: sarangap .a_t. mst.edu
www: web.mst.edu/~sarangap/ |
 | Stefan Schaal
Computer Science, Neuroscience , and Biomedical Engineering
University of Southern California
3710 S. McClintock Ave
Los Angeles, California 90089-2905, USA
phone: 310 740 1976
fax: 213 740 1510
email: sschaal .a_t. usc.edu
www: www-clmc.usc.edu/~sschaal/ |
 | Athanasios V. Vasilakos
Department of Computer and Telecommunications Eng
University of Western Macedonia,Greece
Krinis 3
N.Erythraia, Greece 14671, Greece
phone: +30 6977449705
email: vasilako .a_t. ath.forthnet.gr |
 | Ganesh Kumar Venayagamoorthy
Real-Time Power and Intelligent Systems Laboratory
Missouri University of Science and Technology
Rolla
Missouri, USA
email: gkumar .a_t. ieee.org |
| Marco Wiering
Department of Artificial Intelligence
University of Groningen
Padualaan 14, De Uithof
Groningen, Nijenborgh 9 9700AK, Netherlands
phone: +31(0)50-3636956
fax: +31(0)50-3636687
email: m.a.wiering .a_t. rug.nl
www: www.ai.rug.nl/~mwiering |
 | Donald C. Wunsch II
Dept. of Electrical and Computer Engineering
Missouri University of Science & Technology
301 W. 16th St, 131 EECH
Rolla MO 65409, USA
phone: 573-341-4521
fax: 573-341-4532
email: wunsch .a_t. ieee.org
www: www.linkedin.com/in/wunsch |
(3) Task Forces
3.1 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.
3.2 Reinforcement Learning and Function Approximation
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.
3.3 Robot Reinforcement Learning
| Stefan Schaal, Vice Chair
Computer Science, Neuroscience , and Biomedical Engineering
University of Southern California
3710 S. McClintock Ave
Los Angeles, California 90089-2905, USA
phone: 310 740 1976
fax: 213 740 1510
email: sschaal .a_t. usc.edu
www: www-clmc.usc.edu/~sschaal/ |
Efficient self-improvement by trial and error is a key ability to allow robots to
adapt to their environment and to learn new tricks. Reinforcement learning
offers some of the most general tools in order to fomulate such robot learning
problems while robotics is in theory a natural application domain for reinforcement
learning. However, most reinforcement learning methods cannot be applied
straightforwardly in robotics as the real-world constraints of the domain
are creating exceedingly complex scenarios. On the other hand, robotics offers
an enormous source of inspirations to reinforcement learning, Hence, it is essential
to bring the insights from robotics into reinforcement learning and to create
domain-appropriate reinforcement learning methods for robotics.
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