Reinforcement Learning Using the Stochastic Fuzzy Min–Max Neural Network
Neural Processing Letters
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Neural Processing Letters
A fuzzy Actor-Critic reinforcement learning network
Information Sciences: an International Journal
A reinforcement learning based radial-bassis function network control system
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
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This paper proposes a reinforcement fuzzy adaptive learning control network (RFALCON), constructed by integrating two fuzzy adaptive learning control networks (FALCON), each of which has a feedforward multilayer network and is developed for the realization of a fuzzy controller. One FALCON performs as a critic network (fuzzy predictor), the other as an action network (fuzzy controller). Using temporal difference prediction, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network performs a stochastic exploratory algorithm to adapt itself according to the internal reinforcement signal. An ART-based reinforcement structure/parameter-learning algorithm is developed for constructing the RFALCON dynamically. During the learning process, structure and parameter learning are performed simultaneously. RFALCON can construct a fuzzy control system through a reward/penalty signal. It has two important features; it reduces the combinatorial demands of system adaptive linearization, and it is highly autonomous