NEAT implementation for adapting neural networks applied to ATARI Asteroids
The project's source code can be found in this repository: https://github.com/SantiagoMorenoM/Neat-Python-AsteroidsMasters
- Autores:
-
Moreno Mercado, Santiago
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2023
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/64387
- Acceso en línea:
- http://hdl.handle.net/1992/64387
- Palabra clave:
- Neural Networks
AI
Python
Game
Ingeniería
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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|
| dc.title.none.fl_str_mv |
NEAT implementation for adapting neural networks applied to ATARI Asteroids |
| dc.title.alternative.none.fl_str_mv |
Implementación de NEAT para redes neuronales adaptables aplicadas a ATARI Asteroids |
| title |
NEAT implementation for adapting neural networks applied to ATARI Asteroids |
| spellingShingle |
NEAT implementation for adapting neural networks applied to ATARI Asteroids Neural Networks AI Python Game Ingeniería |
| title_short |
NEAT implementation for adapting neural networks applied to ATARI Asteroids |
| title_full |
NEAT implementation for adapting neural networks applied to ATARI Asteroids |
| title_fullStr |
NEAT implementation for adapting neural networks applied to ATARI Asteroids |
| title_full_unstemmed |
NEAT implementation for adapting neural networks applied to ATARI Asteroids |
| title_sort |
NEAT implementation for adapting neural networks applied to ATARI Asteroids |
| dc.creator.fl_str_mv |
Moreno Mercado, Santiago |
| dc.contributor.advisor.none.fl_str_mv |
Takahashi Rodriguez, Silvia |
| dc.contributor.author.none.fl_str_mv |
Moreno Mercado, Santiago |
| dc.subject.keyword.none.fl_str_mv |
Neural Networks AI Python Game |
| topic |
Neural Networks AI Python Game Ingeniería |
| dc.subject.themes.es_CO.fl_str_mv |
Ingeniería |
| description |
The project's source code can be found in this repository: https://github.com/SantiagoMorenoM/Neat-Python-AsteroidsMasters |
| publishDate |
2023 |
| dc.date.accessioned.none.fl_str_mv |
2023-01-31T14:20:52Z |
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2023-01-31T14:20:52Z |
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2023-01-25 |
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Trabajo de grado - Pregrado |
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info:eu-repo/semantics/bachelorThesis |
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info:eu-repo/semantics/acceptedVersion |
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Text |
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http://purl.org/redcol/resource_type/TP |
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http://purl.org/coar/resource_type/c_7a1f |
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acceptedVersion |
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http://hdl.handle.net/1992/64387 |
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instname:Universidad de los Andes |
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reponame:Repositorio Institucional Séneca |
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http://hdl.handle.net/1992/64387 |
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instname:Universidad de los Andes reponame:Repositorio Institucional Séneca repourl:https://repositorio.uniandes.edu.co/ |
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eng |
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eng |
| dc.relation.references.es_CO.fl_str_mv |
Papavasileiou, Cornelis, J., & Jansen, B. (2021). A Systematic Literature Review of the Successors of "NeuroEvolution of Augmenting Topologies." Evolutionary Computation, 29(1), 1-73. https://doi.org/10.1162/evco_a_00282 NEAT Overview NEAT-Python 0.92 documentation. (s. f.). https://neat-python.readthedocs.io/en/latest/neat_overview.html Formica, F. (2022, Jul 24) finnformica/Asteroids-with-NEAT-python Retrieved from Github: https://github.com/finnformica/Asteroids-with-NEAT-python in November 2022 "The Beach Lab" (Jan 3, 2019) TheBeachLab/asteroids Retrieved from Github: https://github.com/TheBeachLab/asteroids in November 2022 Murray-Smith, D. J. (2012). Experimental modelling: system identification, parameter estimation and model optimisation techniques. Modelling and Simulation of Integrated Systems in Engineering, 165-214. https://doi.org/10.1533/9780857096050.165 Education, I. C. (2021, 3 agosto). Neural Networks. https://www.ibm.com/cloud/learn/neural-networks Education, I. C. (2021a, enero 6). Convolutional Neural Networks. https://www.ibm.com/cloud/learn/convolutional-neural-networks |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf |
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info:eu-repo/semantics/openAccess |
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openAccess |
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18 páginas |
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application/pdf |
| dc.publisher.es_CO.fl_str_mv |
Universidad de los Andes |
| dc.publisher.program.es_CO.fl_str_mv |
Ingeniería de Sistemas y Computación |
| dc.publisher.faculty.es_CO.fl_str_mv |
Facultad de Ingeniería |
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Departamento de Ingeniería Sistemas y Computación |
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Universidad de los Andes |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacionalhttps://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdfinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Takahashi Rodriguez, Silviad781ca31-3a21-42ce-a079-ae7f300551cf600Moreno Mercado, Santiagoabcfb606-8099-431d-b513-fe91ca17902e6002023-01-31T14:20:52Z2023-01-31T14:20:52Z2023-01-25http://hdl.handle.net/1992/64387instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/The project's source code can be found in this repository: https://github.com/SantiagoMorenoM/Neat-Python-AsteroidsMastersThe NEAT algorithm grants useful tools for creating agents that beat simple games, as the agents created through it can give simple and clear outputs, given a set of defined inputs, as well as a fitness/reward formula that is straightforward to design for simple games. Not only that but it's capabilities of generating variable agent "genomes" which take different approaches to the possible hurdle the game presents. This project focuses on developing agents that are able to play the Atari game, Asteroids, with some level of competence, and how these different species fare against the game's hurdles, including the various elements of randomness that the game's setting presents.Ingeniero de Sistemas y ComputaciónPregrado18 páginasapplication/pdfengUniversidad de los AndesIngeniería de Sistemas y ComputaciónFacultad de IngenieríaDepartamento de Ingeniería Sistemas y ComputaciónNEAT implementation for adapting neural networks applied to ATARI AsteroidsImplementación de NEAT para redes neuronales adaptables aplicadas a ATARI AsteroidsTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPNeural NetworksAIPythonGameIngenieríaPapavasileiou, Cornelis, J., & Jansen, B. (2021). A Systematic Literature Review of the Successors of "NeuroEvolution of Augmenting Topologies." Evolutionary Computation, 29(1), 1-73. https://doi.org/10.1162/evco_a_00282NEAT Overview NEAT-Python 0.92 documentation. (s. f.). https://neat-python.readthedocs.io/en/latest/neat_overview.htmlFormica, F. (2022, Jul 24) finnformica/Asteroids-with-NEAT-python Retrieved from Github: https://github.com/finnformica/Asteroids-with-NEAT-python in November 2022"The Beach Lab" (Jan 3, 2019) TheBeachLab/asteroids Retrieved from Github: https://github.com/TheBeachLab/asteroids in November 2022Murray-Smith, D. J. (2012). Experimental modelling: system identification, parameter estimation and model optimisation techniques. Modelling and Simulation of Integrated Systems in Engineering, 165-214. https://doi.org/10.1533/9780857096050.165Education, I. C. (2021, 3 agosto). Neural Networks. https://www.ibm.com/cloud/learn/neural-networksEducation, I. C. (2021a, enero 6). 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