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https://olympias.lib.uoi.gr/jspui/handle/123456789/11127Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Stafylopatis, A. | en |
| dc.contributor.author | Blekas, K. | en |
| dc.date.accessioned | 2015-11-24T17:03:04Z | - |
| dc.date.available | 2015-11-24T17:03:04Z | - |
| dc.identifier.issn | 0377-2217 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/11127 | - |
| dc.rights | Default Licence | - |
| dc.subject | Learning classifier systems | en |
| dc.subject | Genetic algorithms | en |
| dc.subject | Reinforcement learning | en |
| dc.subject | Autonomous navigation | en |
| dc.title | Autonomous vehicle navigation using evolutionary reinforcement learning | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | 10.1016/s0377-2217(97)00372-x | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 1998 | - |
| heal.abstract | Reinforcement learning schemes perform direct on-line search in control space. This makes them appropriate for modifying control rules to obtain improvements in the performance of a system. The effectiveness of a reinforcement learning strategy is studied here through the training of a learning classifier system (LCS) that controls the movement of an autonomous vehicle in simulated paths including left and right turns. The LCS comprises a set of condition-action rules (classifiers) that compete to control the system and evolve by means of a genetic algorithm (GA). Evolution and operation of classifiers depend upon an appropriate credit assignment mechanism based on reinforcement learning. Different design options and the role of various parameters have been investigated experimentally. The performance of vehicle movement under the proposed evolutionary approach is superior compared with that of other (neural) approaches based on reinforcement learning that have been applied previously to the same benchmark problem. | en |
| heal.journalName | European Journal of Operational Research | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| blekas-1996-Autonomous vehicle navigation using evolutionary reinforcement learning.pdf | 1.17 MB | Adobe PDF | View/Open Request a copy |
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