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- W2124554657 abstract "We show that a reinforcement learning method, adaptive critic based approximate dynamic programming, can be used to create fuzzy policy managers for adaptive control of a logistic system. Two different architectures are used for the policy manager, a feed forward neural network, and a fuzzy rule base. For both architectures, policy managers are trained that outperform LP and GA derived fixed policies in stochastic and non-stationary demand environments. In all cases the fuzzy system initialized with expert information outperforms the neural network." @default.
- W2124554657 created "2016-06-24" @default.
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- W2124554657 date "2002-11-13" @default.
- W2124554657 modified "2023-09-24" @default.
- W2124554657 title "Adaptive critic based adaptation of a fuzzy policy manager for a logistic system" @default.
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- W2124554657 doi "https://doi.org/10.1109/nafips.2001.944315" @default.
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