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A3C for drone autonomous driving using Airsim
dc.contributor.author | Villota Miranda, David | |
dc.contributor.author | Gil Martínez, Montserrat | |
dc.contributor.author | Rico-Azagra, Javier | |
dc.date.accessioned | 2021-08-24T11:08:07Z | |
dc.date.available | 2021-08-24T11:08:07Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Villota, D., Gil,. M., Rico, J. A3C for drone autonomous driving using Airsim. En XLII Jornadas de Automática: libro de actas. Castelló, 1-3 de septiembre de 2021 (pp. 203-209). DOI capítulo: https://doi.org/10.17979/spudc.9788497498043.203 DOI libro: https://doi.org/10.17979/spudc.9788497498043 | es_ES |
dc.identifier.isbn | 978-84-9749-804-3 | |
dc.identifier.uri | http://hdl.handle.net/2183/28307 | |
dc.description.abstract | [Abstract] In this work, we apply artificial intelligence to guide a drone to a certain point autonomously. Unreal engine creates a virtual environment where the drone can fly, and the algorithm is trained simulating the drone dynamics thanks to Airsim plugin. The implemented algorithm is Asynchronous Actor-Critic Advantage (A3C), which trains a neural network with less computing resources than standard reinforcement learning algorithms that normally needs costly GPUs. To prove these advantages, several experiments are run using a different number of parallel simulations (threads). The drone should reach a point randomly generated each episode. The reward, the value and the advantage function are used to evaluate the performance. As expected, these experiments show that a higher number of threads helps the leaning process improve and become more stable. These learning results are of interest to optimize the computing resources in future applications. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Universidade da Coruña, Servizo de Publicacións | es_ES |
dc.relation.uri | https://doi.org/10.17979/spudc.9788497498043.203 | es_ES |
dc.rights | Atribución-NoComercial-CompartirIgual 4.0 Internacional https://creativecommons.org/licenses/by-nc-sa/4.0/deed.es | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/es/ | * |
dc.subject | A3C | es_ES |
dc.subject | Actor critic | es_ES |
dc.subject | Reinforcement learning | es_ES |
dc.subject | Autonomous driving | es_ES |
dc.subject | Airsim | es_ES |
dc.subject | Multithread | es_ES |
dc.title | A3C for drone autonomous driving using Airsim | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.startPage | 203 | es_ES |
UDC.endPage | 209 | es_ES |
dc.identifier.doi | https://doi.org/10.17979/spudc.9788497498043.203 | |
UDC.conferenceTitle | XLII Jornadas de Automática | es_ES |