Reinforcement Learning for An ART-Based Fuzzy Adaptive Learning Control Network  

Oleh:

Lin, Cheng-Jian ; Lin, Chin-Teng

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 7 no. 3 (1996), page 709-731.
Topik: optical instruments; reinforcement; learning; ART - based fuzzy; learning control networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.1 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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.

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