This paper introduces NFQ, an algorithm for efficient and effective training of a Q-value function represented by a multi-layer perceptron. Based on the principle of storing and reusing transition experiences, a model-free, neural network based Reinforcement Learning algorithm is proposed. The method is evaluated on three benchmark problems. It is shown empirically, that reasonably few interactions with the plant are needed to generate control policies of high quality. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Riedmiller, M. (2005). Neural fitted Q iteration - First experiences with a data efficient neural Reinforcement Learning method. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3720 LNAI, pp. 317–328). https://doi.org/10.1007/11564096_32
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