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dc.contributorBéjar Alonso, Javier
dc.contributor.authorRoldán Montaner, Carlos
dc.date.accessioned2018-08-30T17:35:20Z
dc.date.available2018-08-30T17:35:20Z
dc.date.issued2018-06
dc.identifier.urihttp://hdl.handle.net/2117/120692
dc.description.abstractEste proyecto intenta desarrollar agentes de aprendizaje por refuerzo para escenarios concretos de Starcraft 2 y sacar conclusiones sobre cuales son las dificultades más importantes y los enfoques más adecuados para afrontar el reto que supone.
dc.description.abstractThis project's goal is to develop reinforcement learning agents for specific scenarios of Starcraft 2 and to draw some conclusions about which are the main difficulties and what are the best approaches to face the challenge that it presents.
dc.language.isospa
dc.publisherUniversitat Politècnica de Catalunya
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshArtificial intelligence
dc.subject.lcshReinforcement learning
dc.subject.otheraprenentatge
dc.subject.otherreforç
dc.subject.otherStarcraft
dc.subject.otherStarcraft 2
dc.subject.otherQ-Learning
dc.subject.otherSARSA
dc.subject.otherDQN
dc.subject.otherIA
dc.titleDeep reinforcement learning IA para Starcraft 2
dc.typeBachelor thesis
dc.subject.lemacIntel·ligència artificial
dc.subject.lemacAprenentatge per reforç
dc.identifier.slug134265
dc.rights.accessOpen Access
dc.date.updated2018-06-27T04:03:03Z
dc.audience.educationlevelGrau
dc.audience.mediatorFacultat d'Informàtica de Barcelona
dc.audience.degreeGRAU EN ENGINYERIA INFORMÀTICA (Pla 2010)


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