By Topic

Markov decision process toolbox

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

4 Author(s)
Shan Peng ; Dept. Electron. Inf. & Control Eng., Guangxi Univ. of Technol., Liuzhou, China ; Li Ran ; Ning Sheng-hua ; Yang Qin

Markov decision process is optimal policy-making process which is based on the Markov process theory of random dynamical systems. It is also a theoretical tool to study optimization problems about multi-stage policy-making process in random environment. For its wide range of applications, developing the Markov decision process toolbox is of great significance for the scientific computing software SCILAB. Markov policy process consists of three main criterions: the expected total reward criterion, discount criterion and average criterion. Finally, taking the toys manufacturers as the example the effectiveness of the method is tested.

Published in:

Open-source Software for Scientific Computation (OSSC), 2009 IEEE International Workshop on

Date of Conference:

18-20 Sept. 2009