Online POMDP Algorithms for Very Large Observation Spaces
Abstract
Partially observable Markov decision process (POMDP) is a powerful modeling tool for formulating problems that require planning under uncertainty. While successful in some problems, POMDP algorithms still do not deal effectively with very large observation spaces, such as those obtained with sensors such as lasers and cameras. The goal of this projectis to develop effective online algorithms for solving partially observable Markov decision processes (POMDPs) with very large observation spaces.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 23, 2016
- Source ID
- FA23861514010
Entities
People
- Wee Sun Lee
Organizations
- Air Force Office of Scientific Research
- National University of Singapore
- United States Air Force