Please use this identifier to cite or link to this item:
|Title:||DESPOT: Online POMDP planning with regularization|
|Source:||Somani, A.,Ye, N.,Hsu, D.,Lee, W.S. (2013). DESPOT: Online POMDP planning with regularization. Advances in Neural Information Processing Systems. ScholarBank@NUS Repository.|
|Abstract:||POMDPs provide a principled framework for planning under uncertainty, but are computationally intractable, due to the "curse of dimensionality" and the "curse of history". This paper presents an online POMDP algorithm that alleviates these difficulties by focusing the search on a set of randomly sampled scenarios. A Determinized Sparse Partially Observable Tree (DESPOT) compactly captures the execution of all policies on these scenarios. Our Regularized DESPOT (R-DESPOT) algorithm searches the DESPOT for a policy, while optimally balancing the size of the policy and its estimated value obtained under the sampled scenarios. We give an output-sensitive performance bound for all policies derived from a DESPOT, and show that R-DESPOT works well if a small optimal policy exists. We also give an anytime algorithm that approximates R-DESPOT. Experiments show strong results, compared with two of the fastest online POMDP algorithms. Source code along with experimental settings are available at http://bigbird.comp.nus.edu.sg/pmwiki/farm/appl/.|
|Source Title:||Advances in Neural Information Processing Systems|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 23, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.