Real World Applications of Reinforcement Learning

A special session of the annual IEEE-INNS International Joint Conference on Neural Networks, IJCNN 2012, part of the 2012 IEEE World Congress on Computational Intelligence, IEEE WCCI 2012, Brisbane, Australia, June 10-15, 2012.

AIM AND SCOPE

Reinforcement Learning (RL) algorithms have long left the tiny grid worlds of the
early years. From robot control to autonomous navigation, research labs have
been applying RL to address increasingly difficult problems, showing that this
paradigm is ready for the real world. In recent years, a number of papers have
shown successful practical applications, in fields as diverse as production control,
finance, scheduling, communications, autonomous vehicle control. While
such examples are relevant, they do not abound, and RL is still far from being
routinely applied as more mature supervised machine learning techniques are.
Moreover, conferences and journals tend to dismiss "mere application" papers
which do not carry relevant contributions at the theoretical level.

With this special session, we intend to gather recent examples of the
application of RL to real-world problems, focusing in particular on
the practical difficulties of applying existing RL algorithms, rather
than on theoretical innovations. The aim is to give an updated picture
of the state of the art of real world applications of RL.

We solicit original submissions describing applications
of all flavors of reinforcement learning and approximate dynamic
programming in a real-world scenario.
Topics of interest include, but are not limited to, the application of:

* Approximate dynamic programming
* Reinforcement learning
* Batch RL
* Policy gradients
* Options learning
* Hierarchical RL
* Multi-objective RL
* Multi-agent RL
* Bandit problem solvers
* Markov Decision Processes

in fields such as

* Industrial control
* Production control
* Automotive control
* Autonomous vehicles control
* Logistics
* Telecommunication networks
* Sensor networks
* Ambient intelligence
* Robotics
* Finance

In preparing your submission, please motivate the use of RL; point out
the difficulties encountered in your implementation; discuss potential
bottlenecks and limitations of your approach; and, if possible, compare
its performance with that of a more traditional method.

IMPORTANT DATES

Submission deadline: Extended to Jan 18, 2012
Acceptance notification: Feb 20, 2012
Final version submission: April 2, 2012
Early registration: April 2, 2012
Conference: June 10-15, 2012

Please check the main WCCI site for updates:
http://www.ieee-wcci2012.org/ieee-wcci2012/

SUBMISSION INSTRUCTIONS

Papers should not exceed 8 pages in the IEEE double column format,
US Letter paper size. Please prepare your submission according to
the common instructions for WCCI conferences, available here:
http://www.ieee-wcci2012.org/ieee-wcci2012/index.php?option=com_content&view=article&id=58&Itemid=67

Please upload your paper via the IJCNN 2012 submission page:
http://ieee-cis.org/conferences/ijcnn2012/upload.php
*selecting the special session as main research topic* :
S35. Real World Applications of Reinforcement Learning

Accepted papers will be published in the WCCI 2012 proceedings.

ORGANIZERS

Matteo Gagliolo, Peter Vrancx and Ann Nowé
AI Lab, Computational Modeling group (CoMo)
Department of Informatics
Vrije Universiteit Brussel (VUB)
Brussels, Belgium