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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Neural Fitted Q Iteration – First Experiences with a Data Efficient Neural Reinforcement Learning Method
verfasst von : Martin Riedmiller
Erschienen in: Machine Learning: ECML 2005
Verlag: Springer Berlin Heidelberg
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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.