Point-Based POMDP Algorithms: Improved Analysis and Implementation
Artificial Intelligence
2012-07-09 v1
Abstract
Existing complexity bounds for point-based POMDP value iteration algorithms focus either on the curse of dimensionality or the curse of history. We derive a new bound that relies on both and uses the concept of discounted reachability; our conclusions may help guide future algorithm design. We also discuss recent improvements to our (point-based) heuristic search value iteration algorithm. Our new implementation calculates tighter initial bounds, avoids solving linear programs, and makes more effective use of sparsity.
Cite
@article{arxiv.1207.1412,
title = {Point-Based POMDP Algorithms: Improved Analysis and Implementation},
author = {Trey Smith and Reid Simmons},
journal= {arXiv preprint arXiv:1207.1412},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI2005)