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Speeding up Reinforcement Learning with Learned Models
| dc.contributor | Martín Muñoz, Mario |
| dc.contributor.author | Pou Mulet, Bartomeu |
| dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
| dc.date.accessioned | 2020-01-26T21:35:58Z |
| dc.date.available | 2020-01-26T21:35:58Z |
| dc.date.issued | 2019-10-16 |
| dc.identifier.uri | http://hdl.handle.net/2117/175740 |
| dc.description.abstract | In this master thesis, we have tried to solve two of most prominent Reinforcement Learning problems: sparse rewards and sample efficiency. The combination of Model Based Reinforcement Learning, Hindsight Experience Replay and off-policy methods is the approach we took to solve the problems. |
| dc.language.iso | eng |
| dc.publisher | Universitat Politècnica de Catalunya |
| dc.subject | Àrees temàtiques de la UPC::Informàtica |
| dc.subject.lcsh | Reinforcement learning |
| dc.subject.lcsh | Algorithms |
| dc.subject.other | Model Based Reinforcement Learning |
| dc.subject.other | Hindsight Experience Replay |
| dc.subject.other | off-policy methods |
| dc.subject.other | sparse rewards |
| dc.subject.other | sample efficiency. |
| dc.title | Speeding up Reinforcement Learning with Learned Models |
| dc.type | Master thesis |
| dc.subject.lemac | Aprenentatge per reforç |
| dc.subject.lemac | Algorismes |
| dc.identifier.slug | 143210 |
| dc.rights.access | Open Access |
| dc.date.updated | 2019-10-28T05:00:50Z |
| dc.audience.educationlevel | Màster |
| dc.audience.mediator | Facultat d'Informàtica de Barcelona |
| dc.audience.degree | MÀSTER UNIVERSITARI EN INTEL·LIGÈNCIA ARTIFICIAL (Pla 2017) |
