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.