Volume 40, Number 2, April-June 2006ROADEF 03
|Page(s)||195 - 234|
|Published online||12 October 2006|
Influence of modeling structure in probabilistic sequential decision problems
ONERA-DCSD, 2 Avenue
Édouard-Belin, 31055 Toulouse, France; e-mail: email@example.com;
Markov Decision Processes (MDPs) are a classical framework for stochastic sequential decision problems, based on an enumerated state space representation. More compact and structured representations have been proposed: factorization techniques use state variables representations, while decomposition techniques are based on a partition of the state space into sub-regions and take advantage of the resulting structure of the state transition graph. We use a family of probabilistic exploration-like planning problems in order to study the influence of the modeling structure on the MDP solution. We first discuss the advantages and drawbacks of a graph based representation of the state space, then present our comparisons of two decomposition techniques, and propose to use a global approach combining both state space factorization and decomposition techniques. On the exploration problem instance, it is proposed to take advantage of the natural topological structure of the navigation space, which is partitioned into regions. A number of local policies are optimized within each region, that become the macro-actions of the global abstract MDP resulting from the decomposition. The regions are the corresponding macro-states in the abstract MDP. The global abstract MDP is obtained in a factored form, combining all the initial MDP state variables and one macro-state “region” variable standing for the different possible macro-states corresponding to the regions. Further research is presently conducted on efficient solution algorithms implementing the same hybrid approach for tackling large size MDPs.
Mathematics Subject Classification: 90C40 / 68T37 / 68T20 / 68R05 / 11Y05 / 68R10
Key words: Probabilistic planning / dynamic programming / Markov decision processes / application to autonomous decision making.
© EDP Sciences, 2006
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.