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Reinforcement learning is a machine learning answer to the optimal control problem. It consists in learning an optimal control policy through interactions with the system to be controlled, the quality of this policy being quantified by the so-called value function. A recurrent subtopic of reinforcement learning is to compute an approximation of this value function when the system is too large for an exact representation. This survey reviews state-of-the-art methods for (parametric) valuedoi:10.1109/tnnls.2013.2247418 pmid:24808468 fatcat:liwiujjbufaijcpge3hzii5234