Output details
11 - Computer Science and Informatics
University College London
Output title
Monte-Carlo Planning in Large POMDPs
Type
E - Conference contribution
DOI
-
Name of conference/published proceedings
Advances in Neural Information Processing Systems 24
Volume number
-
Issue number
-
First page of article
2164
ISSN of proceedings
-
Year of publication
2010
URL
-
Number of additional authors
1
Additional information
<12>Stochastic, partially observable decision-making problems (POMDPs) are considered the hardest class of planning problems. The POMCP algorithm, developed in this paper, was the first general, high-performance planning algorithm for this class of problem. It equalled the state-of-the-art on existing benchmark problems, but was also able to achieve good performance on problems that were orders of magnitude larger (e.g. one problem had 10^60 states). POMCP was proven to converge to the optimal planning solution, with asymptotic bounds on the rate of convergence. Wu, Lee and Hsu used POMCP to win the partially observable track of the 2011 International Probabilistic Planning Competition.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
0
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
