Connectionists: Call For Papers: Symposium on Adaptive Dynamic Programming and Reinforcement Learning

[M.A.Wiering]

 Dear colleagues,
Our apologies for any possible cross-postings of this announcement.

We would like to invite you to submit papers to ADPRL'13, to be held in Singapore 15-19 April 2013,
see: http://ieee-ssci.org(http://ieee-ssci.org/)

2013 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning

 Adaptive (or Approximate) dynamic programming (ADP) is a general
and effective approach for solving optimal control problems by adapting
to uncertain environments over time. ADP optimizes a user-defined cost
function with respect to an adaptive control law, conditioned on prior
knowledge of the system, and its state, in the presence of system
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 an attractive approach in a number of application
domains including optimal control and estimation, operation research,
and computational intelligence. ADP is viewed as a form of
reinforcement learning based on an actor-critic architecture that
optimizes a user-prescribed value online and obtains the resulting
optimal control policy.

Reinforcement learning (RL) algorithms learn to optimize an agent by
letting it interact with an environment and learn from its received
feedback. The goal of the agent is to optimize its accumulated reward
over time, and for this it estimates value functions that predict its
future reward intake when executing a particular policy. Reinforcement
learning techniques can be combined with many different function
approximators and do not assume any a priori knowledge about the
environment. An important aspect in RL is that an agent has to explore
parts of the environment it does not know well, while at the same time
it has to exploit its knowledge to maximize its reward intake. RL
techniques have already been applied successfully for many problems
such as controlling robots, game playing, elevator control, network
routing, and traffic light optimization.






Paper submission: 

  23 Nov 2012     




Decision:                     
05 
Jan 2013    




Final submission:  

   
05 
Feb 2013        



Early Registration:    05 Feb 
2013

	Topics


	The symposium topics include, but are not limited to:


	
		Convergence and performance bounds of ADP
	
		Complexity issues in RL and ADP
	
		Statistical learning and RL, PAC bounds for RL
	
		Monte-Carlo and quasi Monte-Carlo methods
	
		Direct policy search, actor-critic methods
	
		Parsimoneous function representation
	
		Adaptive feature discovery
	
		Learning rules and architectures for RL
	
		Sensitivity analysis for policy gradient estimation
	
		Neuroscience and biologically inspired control
	
		Partially observable Markov decision processes
	
		Distributed intelligent systems
	
		Multi-agent RL systems
	
		Multi-level multi-objective optimization for ADPRL
	
		Kernel methods and value function representation
	
		Applications of ADP and RL

Symposium Chair


	Marco Wiering, University of Groningen, Netherlands





Symposium Co-Chairs




	Jagannathan Sarangapani, Missouri University of Science and Technology, USA



	Huaguang Zhang, Northeastern University, China






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