Reinforcement learning algorithms for autonomous mission accomplishment by unmanned aerial vehicles: a comparative view with DQN, SARSA and A2C

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dc.contributor.authorAguilar Jiménez, Gonzalo
dc.contributor.authorLa Escalera Hueso, Arturo de
dc.contributor.authorGómez Silva, María José
dc.date.accessioned2024-04-05T18:35:47Z
dc.date.available2024-04-05T18:35:47Z
dc.date.issued2023-11-06
dc.description2023 Descuentos MDPI
dc.description.abstractUnmanned aerial vehicles (UAV) can be controlled in diverse ways. One of the most common is through artificial intelligence (AI), which comprises different methods, such as reinforcement learning (RL). The article aims to provide a comparison of three RL algorithms-DQN as the benchmark, SARSA as a same-family algorithm, and A2C as a different-structure one-to address the problem of a UAV navigating from departure point A to endpoint B while avoiding obstacles and, simultaneously, using the least possible time and flying the shortest distance. Under fixed premises, this investigation provides the results of the performances obtained for this activity. A neighborhood environment was selected because it is likely one of the most common areas of use for commercial drones. Taking DQN as the benchmark and not having previous knowledge of the behavior of SARSA or A2C in the employed environment, the comparison outcomes showed that DQN was the only one achieving the target. At the same time, SARSA and A2C did not. However, a deeper analysis of the results led to the conclusion that a fine-tuning of A2C could overcome the performance of DQN under certain conditions, demonstrating a greater speed at maximum finding with a more straightforward structure.
dc.description.departmentDepto. de Arquitectura de Computadores y Automática
dc.description.facultyFac. de Ciencias Físicas
dc.description.refereedTRUE
dc.description.statuspub
dc.identifier.doi10.3390/s23219013
dc.identifier.essn1424-8220
dc.identifier.officialurlhttps://www.mdpi.com/1424-8220/23/21/9013
dc.identifier.urihttps://hdl.handle.net/20.500.14352/102791
dc.issue.number21
dc.journal.titleSensors
dc.language.isoeng
dc.page.final9013-25
dc.page.initial9013-1
dc.publisherMDPI
dc.rightsAttribution 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject.cdu004.8
dc.subject.keywordReinforcement learning
dc.subject.keywordDQN
dc.subject.keywordControl of autonomous vehicle systems
dc.subject.keywordSARSA
dc.subject.keywordA2C
dc.subject.keywordDrone
dc.subject.keywordQuadrotor
dc.subject.keywordUAV
dc.subject.ucmInteligencia artificial (Informática)
dc.subject.unesco1203 Ciencia de Los Ordenadores
dc.titleReinforcement learning algorithms for autonomous mission accomplishment by unmanned aerial vehicles: a comparative view with DQN, SARSA and A2C
dc.typejournal article
dc.type.hasVersionVoR
dc.volume.number23
dspace.entity.typePublication
relation.isAuthorOfPublication779a7137-78a8-46a7-81e0-58b8bd5f1748
relation.isAuthorOfPublication.latestForDiscovery779a7137-78a8-46a7-81e0-58b8bd5f1748
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