[0911.3276v1] An Inverse Method for Policy-Iteration Based Algorithms
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Discrete Mathematics

arXiv:0911.3276v1 (cs)
[Submitted on 17 Nov 2009]

Title:An Inverse Method for Policy-Iteration Based Algorithms

Authors:Laurent Fribourg (LSV Cachan), Etienne André (LSV Cachan)
View PDF
Abstract: We present an extension of two policy-iteration based algorithms on weighted graphs (viz., Markov Decision Problems and Max-Plus Algebras). This extension allows us to solve the following inverse problem: considering the weights of the graph to be unknown constants or parameters, we suppose that a reference instantiation of those weights is given, and we aim at computing a constraint on the parameters under which an optimal policy for the reference instantiation is still optimal. The original algorithm is thus guaranteed to behave well around the reference instantiation, which provides us with some criteria of robustness. We present an application of both methods to simple examples. A prototype implementation has been done.
Subjects: Discrete Mathematics (cs.DM); Logic in Computer Science (cs.LO)
ACM classes: F.4.3; F.4.1
Cite as: arXiv:0911.3276 [cs.DM]
  (or arXiv:0911.3276v1 [cs.DM] for this version)
  https://doi.org/10.48550/arXiv.0911.3276
Journal reference: EPTCS 10, 2009, pp. 44-61
Related DOI: https://doi.org/10.4204/EPTCS.10.4

Submission history

From: EPTCS [view email]
[v1] Tue, 17 Nov 2009 12:46:19 UTC (25 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
cs.DM
< prev   |   next >
new | recent | 2009-11
Change to browse by:
cs
cs.LO

References & Citations

DBLP - CS Bibliography

listing | bibtex
Laurent Fribourg
Étienne André
export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)