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:


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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