By Topic

Optimization in distributed controlled Markov chains

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
$33 $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

2 Author(s)
Junjie Wang ; Hong Kong Univ. of Sci. & Technol., Hong Kong ; Xi-Ren Cao

Performance potential theory has proved to be a promising tool in optimizing the infinite-horizon Markov decision problem (MDP). So far, the research in this area is implicitly focused on a simple system with a single controller. In this paper, we consider the distributed controlled Markov chain, where the system consists of several individual control units and it evolves under the combined control of these nodes. Motivated by practical background, we investigate a structure of MDP with event-dependent decisions. We explore a notion of expanded Markov chain to map this problem to a traditional MDP model. In particular, we address ourselves to the complexity-reduction techniques to deal with the enlarged state space. For the distributed system where a particular node can only access partial system information, we develop some algorithms for decentralized potential estimation and policy iteration

Published in:

Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on  (Volume:3 )

Date of Conference:

11-14 Oct 1998