Publicación:
Constructing a language for testing Reinforcement Learning programs using NLP techniques

Vista sencilla de ítem
authorProfile.id.code200215739
dc.contributor.advisorCardozo Álvarez, Nicolás
dc.contributor.authorMedina Afanador, Luis Alejandro
dc.contributor.juryDusparic, Ivana
dc.contributor.juryManrique Piramanrique, Rubén Francisco
dc.date.accessioned2025-07-30T16:25:06Z
dc.date.available2025-07-30T16:25:06Z
dc.date.issued2025-07-28
dc.description.abstractProbar el buen funcionamiento de un Agente en Reinforcement Learning es un desafío, esta tesis plantea usar técnicas de NLP para generar casos de prueba en donde la posibilidad de fallos es más probable, interpretando que la interacción entre un agente y el ambiente es un lenguaje o una secuencia según convenga.
dc.description.degreelevelMaestría
dc.format.extent66 páginas
dc.format.mimetypeapplication/pdf
dc.identifier.instnameinstname:Universidad de los Andes
dc.identifier.reponamereponame:Repositorio Institucional Séneca
dc.identifier.repourlrepourl:https://repositorio.uniandes.edu.co/
dc.identifier.urihttps://hdl.handle.net/1992/76816
dc.language.isoeng
dc.publisherUniversidad de los Andes
dc.publisher.departmentDepartamento de Ingeniería de Sistemas y Computación
dc.publisher.facultyFacultad de Ingeniería
dc.publisher.programMaestría en Ingeniería de Sistemas y Computación
dc.relation.referencesLionel C. Briand Fellow IEEE Mojtaba Bagherzadeh Amirhossein Zolfagharian, Manel Abdellatif and Ramesh S. A Search-Based Testing Approach for Deep Reinforcement Learning Agents. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, vol. 49, no. 7 edition, 2023. ISBN 0-201-37921-X.
dc.relation.referencesMatteo Biagiola and Paolo Tonella. Testing the Plasticity of Reinforcement Learning Based Systems. Università della Svizzera italiana, Switzerland, revised edition, 2022. ISBN 0-201- 37921-X.
dc.relation.referencesMatteo Biagiola and Paolo Tonella. Testing of Deep Reinforcement Learning Agents with Surrogate Models. Università della Svizzera italiana, Switzerland, revised edition, 2023. ISBN 0-201-37921-X.
dc.relation.referencesKapil Chauhan. CartPole_DQN: CartPole_v0 Jupyter Notebook. https://github. com/kapilnchauhan77/CartPole_DQN/blob/master/CartPole_v0.ipynb, 2019. Accessed: May 15, 2025.
dc.relation.referencesIan Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.
dc.relation.referencesIBM. What is natural language processing? https://www.ibm.com/think/topics/ natural-language-processing, n.d. Accessed: May 23, 2025.
dc.relation.referencesMahmood Khordoo. Deep-Reinforcement-Learning-with-PyTorch: nstep DQN for LunarLander-v2. https://github.com/khordoo/ Bibliography Deep-Reinforcement-Learning-with-PyTorch/blob/example/examples/DQN/ lunarlander_v2-dqn-n-step.py, 2020. Accessed: May 15,2025.
dc.relation.referencesShakti Kumar. adaptiveSystems: RL_Benchmarks README. https://github. com/shaktikshri/adaptiveSystems/blob/master/RL_Benchmarks/README.md, 2019. Accessed: May 15, 2025.
dc.relation.referencesEmmanouil D. Oikonomou, Petros Karvelis, Nikolaos Giannakeas, Aristidis Vrachatis, Evripidis Glavas, and Alexandros T. Tzallas. How natural language processing derived techniques are used on biological data: a systematic review. Network Modeling Analysis in Health Informatics and Bioinformatics, 13(23), 2024. DOI 10.1007/s13721-024-00458-1.
dc.relation.referencesAlec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. Improving language understanding by generative pre-training. https://cdn.openai.com/research-covers/ language-unsupervised/language_understanding_paper.pdf, 2018. OpenAI Report.
dc.relation.referencesNihal T. Rao. RL-Double-DQN: Double DQN Implementation for CartPole-v0. https: //github.com/nihal-rao/RL-Double-DQN, 2020. Accessed: May 15, 2025.
dc.relation.referencesSigve Rokenes. learning-rl/gym/lunarlander-v2: DQN Example for LunarLander-v2. https: //github.com/evgiz/learning-rl/tree/master/gym/lunarlander-v2, 2019. Accessed: May 15, 2025.
dc.relation.referencesRichard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. The MIT Press Cambridge, Massachusetts,London, England, revised edition, 2015. ISBN 0-201- 37921-X.
dc.relation.referencesSanket Thakur. LunarLander_DQN: DQN Implementation for LunarLanderv2. https://github.com/sanketsans/openAIenv/blob/master/DQN/LunarLander/ LunarLander_DQN.ipynb, 2020. Accessed: May 15, 2025.
dc.relation.referencesAshish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, volume 30. Curran Asso- 64 Bibliography ciates, Inc., 2017. URL https://papers.nips.cc/paper_files/paper/2017/file/ 3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf.
dc.rightsAttribution 4.0 Internationalen
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject.keywordTesting Reinforcement Learning
dc.subject.keywordReinforcement Learning
dc.subject.keywordTesting Reinforcement Learning with NLP
dc.subject.keywordNLP testing
dc.subject.themesIngenieríaspa
dc.titleConstructing a language for testing Reinforcement Learning programs using NLP techniques
dc.title.alternativeConstructing a language for testing RL
dc.typeTrabajo de grado - Maestría
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.type.redcolhttps://purl.org/redcol/resource_type/TM
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dspace.entity.typePublication
person.identifier.gsidhttps://scholar.google.es/citations?user=3iTzjQsAAAAJ
person.identifier.orcid0000-0002-1094-9952
relation.isDirectorOfPublicationa77ff528-fc33-44d6-9022-814f81ef407a
relation.isDirectorOfPublication.latestForDiscoverya77ff528-fc33-44d6-9022-814f81ef407a
Archivos
Bloque original
Mostrando 1 - 2 de 2
Miniatura
Nombre:
Constructing a language for testing RL.pdf
Tamaño:
2.2 MB
Formato:
Adobe Portable Document Format
Descargar
No hay miniatura disponible
Nombre:
autorización_tesis_aprobada.pdf
Tamaño:
353.06 KB
Formato:
Adobe Portable Document Format
Descripción:
HIDE
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
2.48 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar
Colecciones
Tesis/Trabajos de Grado