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ECAI
2008
Springer

A Simulation-based Approach for Solving Generalized Semi-Markov Decision Processes

8 years 4 months ago
A Simulation-based Approach for Solving Generalized Semi-Markov Decision Processes
Time is a crucial variable in planning and often requires special attention since it introduces a specific structure along with additional complexity, especially in the case of decision under uncertainty. In this paper, after reviewing and comparing MDP frameworks designed to deal with temporal problems, we focus on Generalized Semi-Markov Decision Processes (GSMDP) with observable time. We highlight the inherent structure and complexity of these problems and present the differences with classical reinforcement learning problems. Finally, we introduce a new simulation-based reinforcement learning method for solving GSMDP, bringing together results from simulation-based policy iteration, regression techniques and simulation theory. We illustrate our approach on a subway network control example.
Emmanuel Rachelson, Gauthier Quesnel, Fréd&
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2008
Where ECAI
Authors Emmanuel Rachelson, Gauthier Quesnel, Frédérick Garcia, Patrick Fabiani
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