A programming language for reinforcement learning
The increasing complexity of reinforcement learning (RL) algorithms has revealed a critical gap in the programming models and tools available to developers, leading to suboptimal implementations and a lack of standardization in RL software projects. This thesis proposes the design and implementation...
- Autores:
-
Morillo Cervantes, Camilo Andrés
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2025
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/75480
- Acceso en línea:
- https://hdl.handle.net/1992/75480
- Palabra clave:
- Reinforcement learning
Programming language
Abstraction
Ingeniería
- Rights
- openAccess
- License
- Attribution 4.0 International
| id |
UNIANDES2_5f381356c7ded4b4bace813a29d89f6e |
|---|---|
| oai_identifier_str |
oai:repositorio.uniandes.edu.co:1992/75480 |
| network_acronym_str |
UNIANDES2 |
| network_name_str |
Séneca: repositorio Uniandes |
| repository_id_str |
|
| dc.title.eng.fl_str_mv |
A programming language for reinforcement learning |
| title |
A programming language for reinforcement learning |
| spellingShingle |
A programming language for reinforcement learning Reinforcement learning Programming language Abstraction Ingeniería |
| title_short |
A programming language for reinforcement learning |
| title_full |
A programming language for reinforcement learning |
| title_fullStr |
A programming language for reinforcement learning |
| title_full_unstemmed |
A programming language for reinforcement learning |
| title_sort |
A programming language for reinforcement learning |
| dc.creator.fl_str_mv |
Morillo Cervantes, Camilo Andrés |
| dc.contributor.advisor.none.fl_str_mv |
Cardozo Álvarez, Nicolás |
| dc.contributor.author.none.fl_str_mv |
Morillo Cervantes, Camilo Andrés |
| dc.subject.keyword.eng.fl_str_mv |
Reinforcement learning Programming language Abstraction |
| topic |
Reinforcement learning Programming language Abstraction Ingeniería |
| dc.subject.themes.spa.fl_str_mv |
Ingeniería |
| description |
The increasing complexity of reinforcement learning (RL) algorithms has revealed a critical gap in the programming models and tools available to developers, leading to suboptimal implementations and a lack of standardization in RL software projects. This thesis proposes the design and implementation of a specialized programming language that provides higher-level abstractions tailored specifically for RL. By addressing the representation of state and action space, the reward function and the management of hyperparameters, our language aims to alleviate the burden on programmers, allowing them to concentrate on the intrinsic complexities of their algorithms rather than the underlying details of RL. We will develop language abstractions and data structures within the Racket programming environment to facilitate the expression of RL constructs effectively. Additionally, a suite of test programs will be created to evaluate the efficacy and usability of our proposed language. This work seeks to enhance the quality and accessibility of RL programming, fostering improved practices and innovations in the field. |
| publishDate |
2025 |
| dc.date.accessioned.none.fl_str_mv |
2025-01-20T14:11:43Z |
| dc.date.available.none.fl_str_mv |
2025-01-20T14:11:43Z |
| dc.date.issued.none.fl_str_mv |
2025-01-17 |
| dc.type.none.fl_str_mv |
Trabajo de grado - Pregrado |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
| dc.type.version.none.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.coar.none.fl_str_mv |
http://purl.org/coar/resource_type/c_7a1f |
| dc.type.content.none.fl_str_mv |
Text |
| dc.type.redcol.none.fl_str_mv |
http://purl.org/redcol/resource_type/TP |
| format |
http://purl.org/coar/resource_type/c_7a1f |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/1992/75480 |
| dc.identifier.instname.none.fl_str_mv |
instname:Universidad de los Andes |
| dc.identifier.reponame.none.fl_str_mv |
reponame:Repositorio Institucional Séneca |
| dc.identifier.repourl.none.fl_str_mv |
repourl:https://repositorio.uniandes.edu.co/ |
| url |
https://hdl.handle.net/1992/75480 |
| identifier_str_mv |
instname:Universidad de los Andes reponame:Repositorio Institucional Séneca repourl:https://repositorio.uniandes.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.none.fl_str_mv |
Nicolás Cardozo, Ivana Dusparic, and Christian Cabrera. Prevalence of code smells in reinforcement learning projects. IEEE/ACM 2nd International Conference on AI Engineering – Software Engineering for AI (CAIN), 2023. Lyn Dupré. BUGS in Writing: A Guide to Debugging Your Prose. Revised edition, 1998. ISBN 0-201-37921-X. Damien Ernst and Arthur Louette. Introduction to reinforcement learning. Feuerriegel, S., Hartmann, J., Janiesch, C., and Zschech, P, pages 111–126, 2024. Matthias Felleisen, Robert Bruce Findler, Matthew Flatt, Shriram Krishnamurthi, Eli Barzilay, Jay McCarthy, and Sam Tobin-Hochstadt. The racket manifesto. In 1st Summit on Advances in Programming Languages (SNAPL 2015). Schloss-Dagstuhl-Leibniz Zentrum für Informatik, 2015. Joan Giner-Miguelez, Abel Gómez, and Jordi Cabot. A domain-specific language for describing machine learning datasets. Journal of Computer Languages, 76:101209, 2023. Sagar Imambi, Kolla Bhanu Prakash, and GR Kanagachidambaresan. Pytorch. Programming with TensorFlow: solution for edge computing applications, pages 87–104, 2021. Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael Jordan, and Ion Stoica. Rllib: Abstractions for distributed reinforcement learning. In International conference on machine learning, pages 3053–3062. PMLR, 2018. Abhishek Nandy, Manisha Biswas, Abhishek Nandy, and Manisha Biswas. Reinforcement learning with keras, tensorflow, and chainerrl. Reinforcement learning: With open ai, tensorflow and keras using python, pages 129–153, 2018. Kate Nussenbaum and Catherine A Hartley. Reinforcement learning across development: What insights can we draw from a decade of research? Developmental cognitive neuroscience, 40:100733, 2019. Bo Pang, Erik Nijkamp, and Ying Nian Wu. Deep learning with tensorflow: A review. Journal of Educational and Behavioral Statistics, 45(2):227–248, 2020. Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, and Noah Dormann. Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research, 22(268):1–8, 2021. William Strunk and E.B. White. The Elements of Style. Longman, fourth edition, 2000. ISBN 0-205-30902-X. Mark Towers, Ariel Kwiatkowski, Jordan Terry, John U Balis, Gianluca De Cola, Tristan Deleu, Manuel Goulao, Andreas Kallinteris, Markus Krimmel, Arjun KG, et al. Gymnasium: A standard interface for reinforcement learning environments. arXiv preprint arXiv:2407.17032, 2024. |
| dc.rights.en.fl_str_mv |
Attribution 4.0 International |
| dc.rights.uri.none.fl_str_mv |
http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.accessrights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.coar.none.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| rights_invalid_str_mv |
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.none.fl_str_mv |
33 páginas |
| dc.format.mimetype.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Universidad de los Andes |
| dc.publisher.program.none.fl_str_mv |
Ingeniería de Sistemas y Computación |
| dc.publisher.faculty.none.fl_str_mv |
Facultad de Ingeniería |
| dc.publisher.department.none.fl_str_mv |
Departamento de Ingeniería de Sistemas y Computación |
| publisher.none.fl_str_mv |
Universidad de los Andes |
| institution |
Universidad de los Andes |
| bitstream.url.fl_str_mv |
https://repositorio.uniandes.edu.co/bitstreams/9125cd43-b29b-4d5e-87d6-caaea8a3d0de/download https://repositorio.uniandes.edu.co/bitstreams/578da201-def7-4dc2-a70c-dfd2e14d72b5/download https://repositorio.uniandes.edu.co/bitstreams/d4abe52c-8e02-44af-ba16-d1c504f51b64/download https://repositorio.uniandes.edu.co/bitstreams/55bc5a62-2331-4e2a-abcb-2cedde0a3ed7/download https://repositorio.uniandes.edu.co/bitstreams/28e498dd-1823-448b-aef8-c4bb49436657/download https://repositorio.uniandes.edu.co/bitstreams/942318c3-013d-4449-8dad-f9b0802b62f5/download https://repositorio.uniandes.edu.co/bitstreams/81c9f051-45d8-4966-83bf-b194720a4a24/download https://repositorio.uniandes.edu.co/bitstreams/4a8f136d-0b5a-41bf-8cc6-50e9fcc2ed7d/download |
| bitstream.checksum.fl_str_mv |
a3da7d993df25f34c1c86fa6f12cb209 84ab6f78b6f215f43579574a3766406b 0175ea4a2d4caec4bbcc37e300941108 ae9e573a68e7f92501b6913cc846c39f 1bf9b1c4074d9f27cc273178418e0709 c19ac71db4cb849b8ce8b71f92f8a3f7 f296c6efa77367b9a7afebc503a12629 e3db4ff7eeda924f49a609528991aafc |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio institucional Séneca |
| repository.mail.fl_str_mv |
adminrepositorio@uniandes.edu.co |
| _version_ |
1828159279970910208 |
| spelling |
Cardozo Álvarez, Nicolásvirtual::22188-1Morillo Cervantes, Camilo Andrés2025-01-20T14:11:43Z2025-01-20T14:11:43Z2025-01-17https://hdl.handle.net/1992/75480instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/The increasing complexity of reinforcement learning (RL) algorithms has revealed a critical gap in the programming models and tools available to developers, leading to suboptimal implementations and a lack of standardization in RL software projects. This thesis proposes the design and implementation of a specialized programming language that provides higher-level abstractions tailored specifically for RL. By addressing the representation of state and action space, the reward function and the management of hyperparameters, our language aims to alleviate the burden on programmers, allowing them to concentrate on the intrinsic complexities of their algorithms rather than the underlying details of RL. We will develop language abstractions and data structures within the Racket programming environment to facilitate the expression of RL constructs effectively. Additionally, a suite of test programs will be created to evaluate the efficacy and usability of our proposed language. This work seeks to enhance the quality and accessibility of RL programming, fostering improved practices and innovations in the field.Pregrado33 páginasapplication/pdfengUniversidad de los AndesIngeniería de Sistemas y ComputaciónFacultad de IngenieríaDepartamento de Ingeniería de Sistemas y ComputaciónAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2A programming language for reinforcement learningTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPReinforcement learningProgramming languageAbstractionIngenieríaNicolás Cardozo, Ivana Dusparic, and Christian Cabrera. Prevalence of code smells in reinforcement learning projects. IEEE/ACM 2nd International Conference on AI Engineering – Software Engineering for AI (CAIN), 2023.Lyn Dupré. BUGS in Writing: A Guide to Debugging Your Prose. Revised edition, 1998. ISBN 0-201-37921-X.Damien Ernst and Arthur Louette. Introduction to reinforcement learning. Feuerriegel, S., Hartmann, J., Janiesch, C., and Zschech, P, pages 111–126, 2024.Matthias Felleisen, Robert Bruce Findler, Matthew Flatt, Shriram Krishnamurthi, Eli Barzilay, Jay McCarthy, and Sam Tobin-Hochstadt. The racket manifesto. In 1st Summit on Advances in Programming Languages (SNAPL 2015). Schloss-Dagstuhl-Leibniz Zentrum für Informatik, 2015.Joan Giner-Miguelez, Abel Gómez, and Jordi Cabot. A domain-specific language for describing machine learning datasets. Journal of Computer Languages, 76:101209, 2023.Sagar Imambi, Kolla Bhanu Prakash, and GR Kanagachidambaresan. Pytorch. Programming with TensorFlow: solution for edge computing applications, pages 87–104, 2021.Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael Jordan, and Ion Stoica. Rllib: Abstractions for distributed reinforcement learning. In International conference on machine learning, pages 3053–3062. PMLR, 2018.Abhishek Nandy, Manisha Biswas, Abhishek Nandy, and Manisha Biswas. Reinforcement learning with keras, tensorflow, and chainerrl. Reinforcement learning: With open ai, tensorflow and keras using python, pages 129–153, 2018.Kate Nussenbaum and Catherine A Hartley. Reinforcement learning across development: What insights can we draw from a decade of research? Developmental cognitive neuroscience, 40:100733, 2019.Bo Pang, Erik Nijkamp, and Ying Nian Wu. Deep learning with tensorflow: A review. Journal of Educational and Behavioral Statistics, 45(2):227–248, 2020.Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, and Noah Dormann. Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research, 22(268):1–8, 2021.William Strunk and E.B. White. The Elements of Style. Longman, fourth edition, 2000. ISBN 0-205-30902-X.Mark Towers, Ariel Kwiatkowski, Jordan Terry, John U Balis, Gianluca De Cola, Tristan Deleu, Manuel Goulao, Andreas Kallinteris, Markus Krimmel, Arjun KG, et al. Gymnasium: A standard interface for reinforcement learning environments. arXiv preprint arXiv:2407.17032, 2024.202015224Publicationhttps://scholar.google.es/citations?user=3iTzjQsAAAAJvirtual::22188-10000-0002-1094-9952virtual::22188-1a77ff528-fc33-44d6-9022-814f81ef407avirtual::22188-1a77ff528-fc33-44d6-9022-814f81ef407avirtual::22188-1ORIGINALautorizacion tesis.pdfautorizacion tesis.pdfHIDEapplication/pdf317691https://repositorio.uniandes.edu.co/bitstreams/9125cd43-b29b-4d5e-87d6-caaea8a3d0de/downloada3da7d993df25f34c1c86fa6f12cb209MD51A programming language for reinforcement learning.pdfA programming language for reinforcement learning.pdfapplication/pdf599138https://repositorio.uniandes.edu.co/bitstreams/578da201-def7-4dc2-a70c-dfd2e14d72b5/download84ab6f78b6f215f43579574a3766406bMD52CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8908https://repositorio.uniandes.edu.co/bitstreams/d4abe52c-8e02-44af-ba16-d1c504f51b64/download0175ea4a2d4caec4bbcc37e300941108MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-82535https://repositorio.uniandes.edu.co/bitstreams/55bc5a62-2331-4e2a-abcb-2cedde0a3ed7/downloadae9e573a68e7f92501b6913cc846c39fMD54TEXTautorizacion tesis.pdf.txtautorizacion tesis.pdf.txtExtracted texttext/plain1967https://repositorio.uniandes.edu.co/bitstreams/28e498dd-1823-448b-aef8-c4bb49436657/download1bf9b1c4074d9f27cc273178418e0709MD55A programming language for reinforcement learning.pdf.txtA programming language for reinforcement learning.pdf.txtExtracted texttext/plain36787https://repositorio.uniandes.edu.co/bitstreams/942318c3-013d-4449-8dad-f9b0802b62f5/downloadc19ac71db4cb849b8ce8b71f92f8a3f7MD57THUMBNAILautorizacion tesis.pdf.jpgautorizacion tesis.pdf.jpgGenerated Thumbnailimage/jpeg10957https://repositorio.uniandes.edu.co/bitstreams/81c9f051-45d8-4966-83bf-b194720a4a24/downloadf296c6efa77367b9a7afebc503a12629MD56A programming language for reinforcement learning.pdf.jpgA programming language for reinforcement learning.pdf.jpgGenerated Thumbnailimage/jpeg3548https://repositorio.uniandes.edu.co/bitstreams/4a8f136d-0b5a-41bf-8cc6-50e9fcc2ed7d/downloade3db4ff7eeda924f49a609528991aafcMD581992/75480oai:repositorio.uniandes.edu.co:1992/754802025-03-05 10:02:00.108http://creativecommons.org/licenses/by/4.0/Attribution 4.0 Internationalopen.accesshttps://repositorio.uniandes.edu.coRepositorio institucional Sénecaadminrepositorio@uniandes.edu.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 |