Reinforcement learning for high-level fuzzy petri nets

Abstract

The author has developed a reinforcement learning algorithm for the high-level fuzzy Petri net (HLFPN) models in order to perform structure and parameter learning simultaneously. In addition to the HLFPN itself, the difference and similarity among a variety of subclasses concerning Petri nets are also discussed. As compared with the fuzzy adaptive learning control network (FALCON), the HLFPN model preserves the advantages that: 1) it offers more flexible learning capability because it is able to model both IF-THEN and IF-THEN-ELSE rules; 2) it allows multiple heterogeneous outputs to be drawn if they exist; 3) it offers a more compact data structure for fuzzy production rules so as to save information storage; and 4) it is able to learn faster due to its structural reduction. Finally, main results are presented in the form of seven propositions and supported by some experiments.