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This paper discusses the problem of optimal control for systems performing in uncertain environments, where little information is available regarding the system dynamics. A reinforcement learning approach is proposed to tackle the problem. A particular method to incorporate Prognostics and Health Management information derived on the system of interest is proposed to improve the reinforcement learning routine. The ideas behind reinforcement learning-based search for optimal control strategies are outlined. A numerical example illustrating the benefits of using PHM information is given.