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Carnegie Mellon Robotics Institute

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Abstract

Recent developments in grid-based and point-based approximation algorithms for POMDPs have greatly improved the tractability of POMDP planning. These approaches operate on sets of belief points by individually learning a value function for each point. In reality, belief points exist in a highly-structured metric simplex, but current POMDP algorithms do not exploit this property. This paper presents a new metric-tree algorithm which can be used in the context of POMDP planning to sort belief points spatially, and then perform fast value function updates over groups of points. We present results showing that this approach can reduce computation in point-based POMDP algorithms for a wide range of problems.

Notes

Associated Lab(s) / Group(s): Robot Learning Lab

Number of pages: 8

Text Reference

Joelle Pineau, Geoffrey Gordon, and Sebastian Thrun, "Applying Metric-Trees to Belief-Point POMDPs," Neural Information Processing Systems (NIPS), 2003.

BibTeX Reference

@inproceedings{Pineau_2003_4825,
   author = "Joelle Pineau and Geoffrey Gordon and Sebastian Thrun",
   title = "Applying Metric-Trees to Belief-Point POMDPs",
   booktitle = "Neural Information Processing Systems (NIPS)",
   year = "2003",
}

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