Intelligent control of a quadrotor with proximal policy optimization reinforcement learning

dc.contributor.authorLopes, Guilherme Cano
dc.contributor.authorFerreira, Murillo [UNESP]
dc.contributor.authorDa Silva Simoes, Alexandre [UNESP]
dc.contributor.authorColombini, Esther Luna [UNESP]
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2019-10-06T15:33:05Z
dc.date.available2019-10-06T15:33:05Z
dc.date.issued2018-12-24
dc.description.abstractAerial platforms, such as quadrotors, are inherently unstable systems. Generally, the task of stabilizing the flight of a quadrotor is approached by techniques based on classic and modern control algorithms. However, recent model-free reinforcement learning algorithms have been successfully used for controlling drones. In this work we show the feasibility of applying reinforcement learning methods to optimize a stochastic control policy (during training), in order to perform the position control of the 'model-free' quadrotor. This process is achieved while maintaining a good sampling efficiency, allowing fast convergence even when using computationally expensive off-The-shelf simulators for robotics and without the necessity of any additional exploration strategy. We used the Proximal Policy Optimization (PPO) algorithm to make the agent learn a reliable control policy. The experiments for the resultant intelligent controller were performed using the V-REP simulator and the Vortex physics engine.en
dc.description.affiliationInstitute of Computing State University of Campinas-UNICAMP
dc.description.affiliationInstitute of Science and Technology of Sorocaba-ICTS São Paulo State University-UNESP
dc.description.affiliationUnespInstitute of Science and Technology of Sorocaba-ICTS São Paulo State University-UNESP
dc.format.extent509-514
dc.identifierhttp://dx.doi.org/10.1109/LARS/SBR/WRE.2018.00094
dc.identifier.citationProceedings - 15th Latin American Robotics Symposium, 6th Brazilian Robotics Symposium and 9th Workshop on Robotics in Education, LARS/SBR/WRE 2018, p. 509-514.
dc.identifier.doi10.1109/LARS/SBR/WRE.2018.00094
dc.identifier.scopus2-s2.0-85061334198
dc.identifier.urihttp://hdl.handle.net/11449/187338
dc.language.isoeng
dc.relation.ispartofProceedings - 15th Latin American Robotics Symposium, 6th Brazilian Robotics Symposium and 9th Workshop on Robotics in Education, LARS/SBR/WRE 2018
dc.rights.accessRightsAcesso restrito
dc.sourceScopus
dc.subjectControl
dc.subjectProximal Policy Optimization
dc.subjectQuadrotor
dc.subjectReinforcement Learning
dc.titleIntelligent control of a quadrotor with proximal policy optimization reinforcement learningen
dc.typeTrabalho apresentado em evento

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