dc.contributor.advisor | Pasquier, Michel | |
dc.contributor.advisor | Barlas, Gerassimos | |
dc.contributor.author | Alhusin, Mohammed Omer Alamin | |
dc.date.accessioned | 2020-01-26T08:05:35Z | |
dc.date.available | 2020-01-26T08:05:35Z | |
dc.date.issued | 2019-12 | |
dc.identifier.other | 35.232-2019.65 | |
dc.identifier.uri | http://hdl.handle.net/11073/16577 | |
dc.description | A Master of Science thesis in Computer Engineering by Mohammed Omer Alamin Alhusin entitled, “Multi Agent Reinforcement Learning Approach for Autonomous Fleet Management”, submitted in December 2019. Thesis advisor is Dr. Michel Pasquier and thesis co-advisor is Dr. Gerassimos Barlas. Soft copy is available (Thesis, Approval Signatures, Completion Certificate, and AUS Archives Consent Form). | en_US |
dc.description.sponsorship | College of Engineering | en_US |
dc.description.sponsorship | Department of Computer Science and Engineering | en_US |
dc.language.iso | en_US | en_US |
dc.relation.ispartofseries | Master of Science in Computer Engineering (MSCoE) | en_US |
dc.subject | Markov Decision Process | en_US |
dc.subject | Markov Game | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Multi-Agent Reinforcement Learning | en_US |
dc.subject | Convolutional Neural Network | en_US |
dc.subject | Fleet Management | en_US |
dc.subject | Taxi Dispatch Problem | en_US |
dc.title | Multi Agent Reinforcement Learning Approach for Autonomous Fleet Management | en_US |
dc.type | Thesis | en_US |