Laurent Péret

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In the past, Markov Decision Processes (MDPs) have become a standard for solving problems of sequential decision under uncertainty. The usual request in this framework is the computation of an optimal policy that defines the optimal action for every state of the system. For complex MDPs, exact computation of optimal policies is often untractable. Several(More)
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