EconBiz - Find Economic Literature
    • Logout
    • Change account settings
  • A-Z
  • Beta
  • About EconBiz
  • News
  • Thesaurus (STW)
  • Academic Skills
  • Help
  •  My account 
    • Logout
    • Change account settings
  • Login
EconBiz - Find Economic Literature
Publications Events
Search options
Advanced Search history
My EconBiz
Favorites Loans Reservations Fines
    You are here:
  • Home
  • Search: subject:"Sample-path reward criterion"
Narrow search

Narrow search

Year of publication
Subject
All
Discrete-time Markov decision process 2 Optimal stationary policy 2 Sample-path reward criterion 2 Unbounded reward 2 Variance-maximization 2
Online availability
All
Undetermined 2
Type of publication
All
Article 2
Language
All
Undetermined 2
Author
All
Zhu, Q. 2
Published in...
All
Computational Statistics 1 Mathematical Methods of Operations Research 1
Source
All
RePEc 2
Showing 1 - 2 of 2
Cover Image
Sample-path optimality and variance-maximization for Markov decision processes
Zhu, Q. - In: Mathematical Methods of Operations Research 65 (2007) 3, pp. 519-538
This paper studies both the average sample-path reward (ASPR) criterion and the limiting average variance criterion for denumerable discrete-time Markov decision processes. The rewards may have neither upper nor lower bounds. We give sufficient conditions on the system’s primitive data and...
Persistent link: https://www.econbiz.de/10010950302
Saved in:
Cover Image
Sample-path optimality and variance-maximization for Markov decision processes
Zhu, Q. - In: Computational Statistics 65 (2007) 3, pp. 519-538
This paper studies both the average sample-path reward (ASPR) criterion and the limiting average variance criterion for denumerable discrete-time Markov decision processes. The rewards may have neither upper nor lower bounds. We give sufficient conditions on the system’s primitive data and...
Persistent link: https://www.econbiz.de/10010759507
Saved in:
A service of the
zbw
  • Sitemap
  • Plain language
  • Accessibility
  • Contact us
  • Imprint
  • Privacy

Loading...