Optimistic planning for sparsely stochastic systems


Reference:
L. Busoniu, R. Munos, B. De Schutter, and R. Babuska, "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 file (82.5 KB)
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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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