A reinforcement learning approach based on the fuzzy min-max neural network
| dc.contributor.author | Likas, A. | en |
| dc.contributor.author | Blekas, K. | en |
| dc.date.accessioned | 2015-11-24T17:02:48Z | |
| dc.date.available | 2015-11-24T17:02:48Z | |
| dc.identifier.issn | 1370-4621 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/11095 | |
| dc.rights | Default Licence | - |
| dc.subject | fuzzy min-max neural network | en |
| dc.subject | reinforcement learning | en |
| dc.subject | autonomous vehicle navigation | en |
| dc.title | A reinforcement learning approach based on the fuzzy min-max neural network | en |
| heal.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. | en |
| heal.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.journalName | Neural Processing Letters | en |
| heal.journalType | peer reviewed | - |
| heal.language | en | - |
| heal.publicationDate | 1996 | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.type | journalArticle | - |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.type.en | Journal article | en |
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