Adaptive Critic Based Adaptation of A Fuzzy Policy Manager for A Logistic System

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. Index terms -applications, neural networks, reinforcement learning, genetic algorithms, qualitative reasoning, rule learning