ADPRL 2016 - 2016 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (IEEE ADPRL'16)

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.
Special Sessions
We are extremely interested in special sessions on topics relevant to the conference, in addition to the regular sessions. Proposals for special sessions should include:
A title, together with a mention that the proposal is for the ADPRL symposium.
A brief description, rationale or motivation for the session.
List of topics and scope.
Importance and relevance to SSCI and ADPRL, possibly included in the description above.
A tentative list of contributions, with the names of authors who have already been invited to participate.
Short biosketches of the proposers.
Please submit your special session proposal to any of the ADPRL organizers before 18 April 2016. Proposals will be reviewed for quality and coherence with the conference topics, and the organizers will be notified of the decision.
Organisers
Dongbin Zhao
University of Chinese Academy of Sciences, China,
Email: zhao-AT-ia.ac.cn
Madalina Drugan
Vrije Universiteit Brussel, Belgium, E-mail: mdrugan-AT-vub.ac.be