Connectionists: CFP: 2015 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning

[Wiering, M.A.]

*CALL FOR PAPERS*

*2015 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement
Learning*
http://adprl15.net
Part of IEEE Symposium Series on Computational Intelligence 2015, Cape
Town, South Africa, 7-10 December 2015

Paper submission: 14 June 2015

Adaptive dynamic programming (ADP) and reinforcement learning (RL) are two
related paradigms for solving decision making problems where a performance
index must be optimized over time. ADP and RL methods are enjoying a
growing popularity and success in applications, fueled by their ability to
deal with complex problems, including features such as uncertainty,
stochastic effects, and nonlinearity. ADP tackles these challenges by
developing optimal control methods that adapt to uncertain systems over
time. RL takes the perspective of an agent that optimizes its behavior by
interacting with an initially unknown environment and learning from the
feedback received. The ability to improve performance over time in
uncertain or unknown environments has led to successful applications of ADP
and RL in areas such as robotics, game playing, automotive engines,
networks, logistics, etc.

The goal of the IEEE Symposium on ADPRL is to provide an outlet and a forum
for interaction between researchers and practitioners in ADP and RL, in
which the two fields are brought together and their connections are
exploited. We equally welcome contributions from control theory, computer
science, operations research, computational intelligence, neuroscience, as
well as other novel perspectives on ADPRL. Original papers are sought on
methods, analysis, applications, and overviews of ADPRL. We are interested
in applications from engineering, artificial intelligence, economics,
medicine, and other relevant fields.

Specific topics of interest include, but are not limited to:
* Convergence and performance analysis
* RL and ADP-based control
* Function approximation and value function representation
* Complexity issues in RL and ADP
* Policy gradient and actor-critic methods
* Direct policy search
* Planning and receding-horizon methods
* Monte-Carlo tree search and other Monte-Carlo methods
* Adaptive feature discovery
* Parsimonious function representation
* Statistical learning and PAC bounds for RL
* Learning rules and architectures
* Bandit techniques for exploration
* Bayesian RL and exploration
* Finite-sample analysis
* Partially observable Markov decision processes
* Neuroscience and biologically inspired control
* ADP and RL for multiplayer games and multiagent systems
* Distributed intelligent systems
* Multi-level multi-objective optimization for ADPRL
* Transfer learning
* Applications of ADP and RL


**Paper Submission Procedure**
Paper submissions should be prepared in the IEEE format and should have at
most 8 pages. Accepted papers will be published in the SSCI proceedings and
on IEEEXplore, http://ieeexplore.ieee.org/, conditioned on registering and
presenting the paper at the conference. Submissions must contain original,
high quality, not submitted or elsewhere published work. Authors must
submit their paper through the IEEE SSCI 2015 webpage
http://ieee-ssci.org.za/

***********************************************************
The call for papers as well as more detailed information can be retrieved
from ADPRL 2015 webpage
http://adprl15.net
and from the main SSCI 2015 webpage
http://ieee-ssci.org.za/

Please make sure you select:
"Adaptive Dynamic Programming and Reinforcement Learning" (ADPRL 2015)
during the  submission process
***********************************************************

**Organisers**

   - Madalina Drugan (Vrije Universiteit Brussel, Belgium) mdrugan at vub.ac.be

   - Marco Wiering (University of Groningen, The Netherlands)
   m.a.wiering at rug.nl
   - Lucian Busoniu (Technical University of Cluj-Napoca, Romania)
   lucian at busoniu.net

**Technical Program Committee**

   - Abhjit Gosavi, Missouri University of Science and Technology, USA
   - Ann Nowé, Vrije Universiteit Brussel, Belgium
   - Boris Defourny, Lehigh University, USA
   - Danil Prokhorov, Toyota Technical Center, USA
   - Dongbin Zhao, Chinese Academy of Sciences, China
   - Draguna Vrabie, United Technologies Research Center, USA
   - Eduardo Alonso, City University London, UK
   - El-Sayed El Alfy, King Fahd University of Petroleum and Minerals,
   Saoudi Arabaia
   - Girish Chowdhary, Oklahoma State University, USA
   - Haibo He, University of Rhode Island, USA
   - Hao Xu, Missouri University of Science and Technology, USA
   - Huaguang Zhang, Northeastern University, China
   - Jagannathan Sarangapani, Missouri University of Science and
   Technology, USA
   - Janey Yu, Massachusetts Institute of Technology, USA
   - Jennie Si, Arizona State University, USA
   - Kang Li, Queen's University Belfast, UK
   - Karl Tuyls, University of Liverpool, UK
   - Kyriakos Vamvoudakis, University of California, Santa Barbara, USA
   - Martijn van Otterlo, Radboud University Nijmegen, Netherlands
   - Martin Riedmiller, University of Freiburg, Germany
   - Matthieu Geist, Supelec Metz, France
   - Philippe Preux, INRIA Lille Nord Europe, France
   - Raphael Fonteneau, University of Liege, Belgium
   - Remi Munos, INRIA Lille Nord Europe, France
   - Shubhendu Bhasin, Indian Institute of Technology Delhi, India
   - Somayeh Moazeni, Stevens Institute of Technology, USA
   - Tobias Jung, University of Liege, Belgium
   - Warren Powell, Princeton University, USA
   - Xin Xu, National University of Defense Technology, China
   - Yanhong Luo, Northeastern University, China
   - Zeng-Guang Hou, Chinese Academy of Sciences, China
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