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The paper re-interprets a family of generative statistical models under the framework of sequential decision making used in reinforcement learning. It develops connections between training algorithms from the two fields. Models following this framework are tested on image datasets and compared to a baseline. The paper proposes a new and interesting view of generative models under the light of sequential decision making. This work clearly opens new perspectives and should allow the formulation of new ways to address learning and inference problems in a broad class of statistical models.