Improving Anytime Point-Based Value Iteration Using Principled Point Selections

James, Michael R.; Samples, Michael E.; Dolgov, Dmitri A.

Improving Anytime Point-Based Value Iteration Using Principled Point Selections

Michael R. James, Michael E. Samples, Dmitri A. Dolgov

BibTeX Cite

Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 865-869.

Abstract

Planning in partially-observable dynamical systems (such as POMDPs and PSRs) is a computationally challenging task. Popular approximation techniques that have proven successful are point-based planning methods including point-based value iteration (PBVI), which works by approximating the solution at a finite set of points. These point-based methods typically are anytime algorithms, whereby an initial solution is obtained using a small set of points, and the solution may be ncrementally improved by including additional points. We introduce a family of anytime PBVI algorithms that use the information present in the current solution for identifying and adding new points that have the potential to best improve the next solution. We motivate and present two different methods for choosing points and evaluate their performance empirically, demonstrating that high-quality solutions can be obtained with significantly fewer points than previous PBVI approaches.

Michael R. James, Michael E. Samples, and Dmitri A. Dolgov. 2007. Improving Anytime Point-Based Value Iteration Using Principled Point Selections. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07. IJCAI, 865–869. https://ijcai.org/proceedings/2007/139

@inproceedings{ijcai2007p139,
  title     = {Improving Anytime Point-Based Value Iteration Using Principled Point Selections},
  author    = {James, Michael R. and Samples, Michael E. and Dolgov, Dmitri A.},
  booktitle = {Proceedings of the Twentieth International Joint Conference on
               Artificial Intelligence, {IJCAI-07}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {Ramon López de Mántaras},
  pages     = {865--869},
  year      = {2007},
  url       = {https://ijcai.org/proceedings/2007/139},
}

TY  - CPAPER
TI  - Improving Anytime Point-Based Value Iteration Using Principled Point Selections
AU  - James, Michael R.
AU  - Samples, Michael E.
AU  - Dolgov, Dmitri A.
T2  - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2007
SP  - 865
EP  - 869
UR  - https://ijcai.org/proceedings/2007/139
ER  -