Optimistic Planning for Sparsely Stochastic Systems
Reference
L. Buşoniu,
R. Munos,
B. De Schutter, and
R. Babuška,
"Optimistic Planning for Sparsely Stochastic Systems," Proceedings of the Workshop on Monte-Carlo Tree Search:
Theory and Applications (MCTS) at the 21st International Conference on
Automated Planning and Scheduling (ICAPS 2011), Freiburg,
Germany, 2 pp., June 2011.
Abstract
We describe an online planning algorithm for finite-action, sparsely
stochastic Markov decision processes, in which the random state
transitions can only end up in a small number of possible next states.
The algorithm builds a planning tree by iteratively expanding states,
where the most promising states are expanded first, in an optimistic procedure aiming to return a good action
after a strictly limited number of expansions. The novel algorithm is
called optimistic planning for sparsely stochastic
systems.
Downloads
- Corresponding technical report:
pdf
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(233 KB)
Bibtex entry
@inproceedings{BusMun:11-041,
author={L. Bu{\c{s}}oniu and R. Munos and B. {D}e Schutter and R.
Babu{\v{s}}ka},
title={Optimistic Planning for Sparsely Stochastic Systems},
booktitle={Proceedings of the Workshop on Monte-Carlo Tree Search: Theory and
Applications (MCTS) at the 21st International Conference on Automated Planning
and Scheduling (ICAPS 2011)},
address={Freiburg, Germany},
month=jun,
year={2011}
}
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Last update: February 21, 2026.