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.
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BibTeX
@inproceedings{BusMun:11-041,
author = {Bu{\c{s}}oniu, Lucian and Munos, R{\'{e}}mi and De Schutter,
Bart and Babu{\v{s}}ka, Robert},
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}
}