Training reinforcement neurocontrollers using the polytope algorithm
| dc.contributor.author | Likas, A. | en |
| dc.contributor.author | Lagaris, I. E. | en |
| dc.date.accessioned | 2015-11-24T17:03:08Z | |
| dc.date.available | 2015-11-24T17:03:08Z | |
| dc.identifier.issn | 1370-4621 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/11134 | |
| dc.rights | Default Licence | - |
| dc.subject | reinforcement learning | en |
| dc.subject | neurocontrol | en |
| dc.subject | optimization | en |
| dc.subject | polytope algorithm | en |
| dc.subject | pole balancing | en |
| dc.subject | genetic reinforcement | en |
| dc.title | Training reinforcement neurocontrollers using the polytope algorithm | en |
| heal.abstract | A new training algorithm is presented for delayed reinforcement learning problems that does not assume the existence of a critic model and employs the polytope optimization algorithm to adjust the weights of the action network so that a simple direct measure of the training performance is maximized. Experimental results from the application of the method to the pole balancing problem indicate improved training performance compared with critic-based and genetic reinforcement approaches. | en |
| heal.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.journalName | Neural Processing Letters | en |
| heal.journalType | peer reviewed | - |
| heal.language | en | - |
| heal.publicationDate | 1999 | - |
| 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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