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Genetic and reinforcement-based rule extraction for regulator control
Published in Publ by IEEE, Piscataway, NJ, United States
Volume: 2
Pages: 1258 - 1263
This paper proposes a novel system for rule extraction of regulator control problems. The system employs a hybrid genetic search and reinforcement learning. The learning strategy requires no supervision and no reference model. The extracted rules are weighted microrules with a discrete nature that constitute a rule-based/table look-up structure capturing control actions. As an example of what the proposed algorithm can learn, we chose the problem of the trailer truck backer-upper. The system is capable of extracting rules that back up the trailer truck from arbitrary initial positions, and show improved performance compared to a neural network controller trained with backprop through time.
About the journal
JournalData powered by TypesetProceedings of the IEEE Conference on Decision and Control
PublisherData powered by TypesetPubl by IEEE, Piscataway, NJ, United States
Open AccessNo