Abstract
The fuzzy min-max neural network constitutes a neural architecture that is based on hyperbox fuzzy sets and can be incrementally trained by appropriately adjusting the number of hyperboxes and their corresponding volumes. Two versions have been proposed: for supervised and unsupervised learning. In this paper a modified approach is presented that is appropriate for reinforcement learning problems with discrete action space and is applied to the difficult task of autonomous vehicle navigation when no a priori knowledge of the enivronment is available. Experimental results indicate that the proposed reinforcement learning network exhibits superior learning behavior compared to conventional reinforcement schemes.
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Likas, A., Blekas, K. A reinforcement learning approach based on the fuzzy min-max neural network. Neural Process Lett 4, 167–172 (1996). https://doi.org/10.1007/BF00426025
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DOI: https://doi.org/10.1007/BF00426025