By Topic

On Solving Optimal Policies for Finite-Stage Event-Based Optimization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Qing-Shan Jia ; Dept. of Autom., Tsinghua Univ., Beijing, China

Event-based optimization (EBO) has been developed to model a specific type of problems, in which decisions can be made only when certain events occur. Because the event sequence usually is not Markovian, how to solve optimal policies for EBOs remains open in general. Motivated by real applications, we focus on finite-stage EBOs with discrete state space in this technical note and make two contributions. First, we show that this EBO can be converted to a partially observable Markov decision process (POMDP). Based on this connection, existing exact and approximate solution methodologies for POMDPs can then be applied to EBOs. Second, we develop the performance difference and derivative formulas and the potential-based policy iteration algorithm, which converges to the global optimum. This algorithm is then applied to a node activation problem in wireless sensor network.

Published in:

Automatic Control, IEEE Transactions on  (Volume:56 ,  Issue: 9 )