ADPRL 2015 - 2015 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (IEEE ADPRL'15)

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 general and 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. A user-defined cost function is optimized with respect to an adaptive control law, conditioned on prior knowledge of the system and its state, in the presence of 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 successful in applications from optimal control and estimation, operation research, and computational intelligence.
RL takes the perspective of an agent that optimizes its behavior by interacting with its environment and learning from the feedback received. The long-term performance is optimized by learning a value function that predicts the future intake of rewards over time. A core feature of RL is that it does not require any a priori knowledge about the environment. Therefore, the agent must explore parts of the environment it does not know well, while at the same time exploiting its knowledge to maximize performance. RL thus provides a framework for learning to behave optimally in unknown environments, which has already been applied to robotics, game playing, network management and traffic control.
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 clear parallels between the two fields are brought together and exploited. We equally welcome contributions from control theory, computer science, operations research, computational intelligence, neuroscience, as well as other novel perspectives on ADPRL. We host original papers on methods, analysis, applications, and overviews of ADPRL. We are interested in applications from engineering, artificial intelligence, economics, medicine, and other relevant fields.
Topics
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
Parsimoneous 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-objective optimization for ADPRL
Transfer learning
Applications of ADP and RL
Accepted papers will be published in the SSCI proceedings and on IEEEXplore, conditioned on registering and presenting the paper at the conference. See Important Dates and Practical Info, which includes the link to the submission site.
Organisers
Madalina Drugan
Vrije Universiteit Brussel, Belgium, E-mail: mdrugan-AT-vub.ac.be
Dr. M.A. (Marco) Wiering
University of Groningen, The Netherlands ,
Email:m.a.wiering-AT-rug.nl
Lucian Busoniu
Technical University of Cluj-Napoca, Romania, Email:lucian-AT-busoniu.net
Program Committee
Raphael Fonteneau, University of Liege, Belgium
Boris Defourny, Lehigh University, USA
Tobias Jung, University of Liege, Belgium
Kyriakos Vamvoudakis, University of California, Santa Barbara, USA
Girish Chowdhary,Oklahoma State University, USA
Xin Xu, National University of Defense Technology, China
Ann Nowé, Vrije Universiteit Brussel, Belgium
Draguna Vrabie, United Technologies Research Center, USA
Matthieu Geist, Supelec Metz, France
Janey Yu, Massachusetts Institute of Technology, USA
Philippe Preux, INRIA Lille Nord Europe, France
Eduardo Alonso,City University London, UK
Shubhendu Bhasin, Indian Institute of Technology Delhi, India
Karl Tuyls,University of Liverpool, UK
Martin Riedmiller, University of Freiburg, Germany
Martijn van Otterlo, Radboud University Nijmegen, Netherlands
Haibo He, University of Rhode Island, USA
Warren Powell, Princeton University, USA
Remi Munos,INRIA Lille Nord Europe, France
Zeng-Guang Hou, Chinese Academy of Sciences, China
Yanhong Luo, Northeastern University, China
Dongbin Zhao, Chinese Academy of Sciences, China
Kang Li, Queen's University Belfast, UK
Danil Prokhorov, Toyota Technical Center, USA
El-Sayed El Alfy, King Fahd University of Petroleum and Minerals, Saudi Arabia
Hao Xu, Missouri University of Science and Technology, USA
Huaguang Zhang, Northeastern University, China
Jagannathan Sarangapani,Missouri University of Science and Technology, USA
Jennie Si, Arizona State University, USA
Somayeh Moazeni, Stevens Institute of Technology, USA
Abhjit Gosavi, Missouri University of Science and Technology, USA