In this thesis, we are interested in sequential decision problems under uncertainty. These problems concern situations of uncertainty where the decision maker has to make several decisions spread over time (i.e., establish a strategy). This problem is much studied in artificial intelligence, known as planning under uncertainty, because of its several applications in many fields (medical diagnosis, artificial players, autopilot, inventory management, ...). The economist community has provided many decision criteria for reasoning under uncertainty in order to compare strategies. However, the difficulties associated with their implementation leads in practice to use criteria less efficients in sequential decision problems. The use of performant criteria is indeed hindered by the lack of efficient algorithms in the computer science literature. The purpose of this thesis is precisely to tackle these algorithmics locks by providing algorithms for optimizing these criteria in sequential decision problems.