Deep learning game agents in Bomberland: A comparative analysis
Bomberland is a modified clone of the popular arcade game Bomberman developed for an AI Agent Competition, adding a layer of cooperation using teams. This project aims to highlight how popular Deep Learning algorithms for game agents, Deep Q Learning and NEAT, perform against added complexity throug...
- Autores:
-
Campo Cotes, Santiago Rafael
- 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:1992/75569
- Acceso en línea:
- https://hdl.handle.net/1992/75569
- Palabra clave:
- Artificial Intelligence
Game Agents
NEAT
Machine Learning
Ingeniería
- Rights
- openAccess
- License
- Attribution 4.0 International
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Deep learning game agents in Bomberland: A comparative analysis |
| title |
Deep learning game agents in Bomberland: A comparative analysis |
| spellingShingle |
Deep learning game agents in Bomberland: A comparative analysis Artificial Intelligence Game Agents NEAT Machine Learning Ingeniería |
| title_short |
Deep learning game agents in Bomberland: A comparative analysis |
| title_full |
Deep learning game agents in Bomberland: A comparative analysis |
| title_fullStr |
Deep learning game agents in Bomberland: A comparative analysis |
| title_full_unstemmed |
Deep learning game agents in Bomberland: A comparative analysis |
| title_sort |
Deep learning game agents in Bomberland: A comparative analysis |
| dc.creator.fl_str_mv |
Campo Cotes, Santiago Rafael |
| dc.contributor.advisor.none.fl_str_mv |
Takahashi Rodríguez, Silvia |
| dc.contributor.author.none.fl_str_mv |
Campo Cotes, Santiago Rafael |
| dc.contributor.jury.none.fl_str_mv |
Takahashi Rodríguez, Silvia |
| dc.subject.keyword.eng.fl_str_mv |
Artificial Intelligence Game Agents NEAT Machine Learning |
| topic |
Artificial Intelligence Game Agents NEAT Machine Learning Ingeniería |
| dc.subject.themes.spa.fl_str_mv |
Ingeniería |
| description |
Bomberland is a modified clone of the popular arcade game Bomberman developed for an AI Agent Competition, adding a layer of cooperation using teams. This project aims to highlight how popular Deep Learning algorithms for game agents, Deep Q Learning and NEAT, perform against added complexity through cooperative, adversarial, and stochastic elements. |
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2025 |
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2025-01-22T16:04:16Z |
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2025-01-22T16:04:16Z |
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2025-01-21 |
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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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https://hdl.handle.net/1992/75569 |
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instname:Universidad de los Andes |
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reponame:Repositorio Institucional Séneca |
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instname:Universidad de los Andes reponame:Repositorio Institucional Séneca repourl:https://repositorio.uniandes.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
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eng |
| dc.relation.references.none.fl_str_mv |
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., & Zaremba, W. (2016, June 6). OpenAI Gym. Retrieved from OpenAI: https://arxiv.org/abs/1606.01540 Gymnasium Team. (2023). Gymnasium. Retrieved from Farama Foundation: https://gymnasium.farama.org/ Krabbenhöft, H. N. (2022, May 12). Bomberland AI - Part I - Lingo, Replays and TensorFlow Observations. Retrieved from Hajos Personal Blog: https://hajo.me/blog/2022/05/12/coder-one-bomberland-tutorial-1of3-introduction-replays-and-conversion-to-tensorflow/ MacWha, R. (2021, March 3). Evolving AIs using a NEAT algorithm. Retrieved from Medium: https://macwha.medium.com/evolving-ais-using-a-neat-algorithm-2d154c623828 Mnih, V., Kavukcuouglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., . . . Antonoglou, I. (2015). Human-level control through deep reinforcement learning. Nature 518, 529-542. OpenAI Five Team. (2018, June 25). OpenAI Five. Retrieved from OpenAI: https://openai.com/research/openai-five Orkin, J. (2006). Three States and a Plan: The A.I. of F.E.A.R. Game Developers Conference. Stanley, K. O., & Miikulainen, R. (2002). Evolving Neural Networks through Augmenting Topologies. Evolutionary Computation 10(2), 99-127. TF-Agents Authors. (2023). Introduction to RL and Deep Q Networks. Retrieved from Tensorflow: https://www.tensorflow.org/agents/tutorials/0_intro_rl The Interactive Agent Team. (2022, November 23). Building Interactive Agents in Video Game Worlds. Retrieved from Google Deepmind: https://deepmind.google/discover/blog/building-interactive-agents-in-video-game-worlds/ Zhang, J. (2021, November 29). Bomberland. Retrieved from CoderOne: https://www.gocoder.one/blog/bomberland-multi-agent-artificial-intelligence-competition/ |
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Attribution 4.0 International |
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16 páginas |
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application/pdf |
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Universidad de los Andes |
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Ingeniería de Sistemas y Computación |
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Facultad de Ingeniería |
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Departamento de Ingeniería de Sistemas y Computación |
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Universidad de los Andes |
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Takahashi Rodríguez, Silviavirtual::22368-1Campo Cotes, Santiago RafaelTakahashi Rodríguez, Silvia2025-01-22T16:04:16Z2025-01-22T16:04:16Z2025-01-21https://hdl.handle.net/1992/75569instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/Bomberland is a modified clone of the popular arcade game Bomberman developed for an AI Agent Competition, adding a layer of cooperation using teams. This project aims to highlight how popular Deep Learning algorithms for game agents, Deep Q Learning and NEAT, perform against added complexity through cooperative, adversarial, and stochastic elements.Pregrado16 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_abf2Deep learning game agents in Bomberland: A comparative analysisTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPArtificial IntelligenceGame AgentsNEATMachine LearningIngenieríaBrockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., & Zaremba, W. (2016, June 6). OpenAI Gym. Retrieved from OpenAI: https://arxiv.org/abs/1606.01540Gymnasium Team. (2023). Gymnasium. Retrieved from Farama Foundation: https://gymnasium.farama.org/Krabbenhöft, H. N. (2022, May 12). Bomberland AI - Part I - Lingo, Replays and TensorFlow Observations. Retrieved from Hajos Personal Blog: https://hajo.me/blog/2022/05/12/coder-one-bomberland-tutorial-1of3-introduction-replays-and-conversion-to-tensorflow/MacWha, R. (2021, March 3). Evolving AIs using a NEAT algorithm. Retrieved from Medium: https://macwha.medium.com/evolving-ais-using-a-neat-algorithm-2d154c623828Mnih, V., Kavukcuouglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., . . . Antonoglou, I. (2015). Human-level control through deep reinforcement learning. Nature 518, 529-542.OpenAI Five Team. (2018, June 25). OpenAI Five. Retrieved from OpenAI: https://openai.com/research/openai-fiveOrkin, J. (2006). Three States and a Plan: The A.I. of F.E.A.R. Game Developers Conference.Stanley, K. O., & Miikulainen, R. (2002). Evolving Neural Networks through Augmenting Topologies. Evolutionary Computation 10(2), 99-127.TF-Agents Authors. (2023). Introduction to RL and Deep Q Networks. Retrieved from Tensorflow: https://www.tensorflow.org/agents/tutorials/0_intro_rlThe Interactive Agent Team. (2022, November 23). Building Interactive Agents in Video Game Worlds. Retrieved from Google Deepmind: https://deepmind.google/discover/blog/building-interactive-agents-in-video-game-worlds/Zhang, J. (2021, November 29). Bomberland. 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