TRABALHOS DE FIM DE CURSO @PUC-Rio
| Título: | COMPARATIVE ANALYSIS BETWEEN EVOLUTIONARY ALGORITHMS AND DEEP REINFORCEMENT LEARNING IN THE AWS DEEPRACER ENVIRONMENT | ||||||||||||
| Autor(es): |
GUSTAVO ARCARY PASSOS |
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| Colaborador(es): |
AUGUSTO CESAR ESPINDOLA BAFFA - Orientador |
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| Catalogação: | 26/MAR/2026 | Língua(s): | PORTUGUESE - BRAZIL |
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| Tipo: | TEXT | Subtipo: | SENIOR PROJECT | ||||||||||
| Notas: |
[pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio. [en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio. |
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| Referência(s): |
[pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/TFCs/consultas/conteudo.php?strSecao=resultado&nrSeq=75851@1 [en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/TFCs/consultas/conteudo.php?strSecao=resultado&nrSeq=75851@2 |
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| DOI: | https://doi.org/10.17771/PUCRio.acad.75851 | ||||||||||||
| Resumo: | |||||||||||||
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This work aims to analyze the performance of deep reinforcement learning
algorithms applied to autonomous driving tasks in simulation environments.
The study uses the AWS DeepRacer platform and its open-source version,
DeepRacer-for-Cloud (DRfC), to perform local training and experiments.
The algorithms Proximal Policy Optimization (PPO) and Soft Actor-Critic
(SAC) were employed, comparing their results across different tracks and
training configurations. The analysis seeks to understand how variations in
hyperparameters and reward functions affect agent performance, as well as to
discuss the advantages and limitations of using a local infrastructure compared
to the AWS cloud service.
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