Deep reinforcement learning for optimal gameplay in street fighter III: a resource-constrained approach
This bachelor’s thesis investigates the performance of reinforcement learning (RL) algorithms in the context of fighting games, specifically Street Fighter III Third Strike, under heavy resource constraints. The research focuses on four distinct RL algorithms: Proximal Policy Optimization (PPO), Asy...
- Autores:
-
Zambrano Huertas, Daniel Ernesto
Díaz Salamanca, Jhoan Sebastián
- 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:1992/70987
- Acceso en línea:
- https://hdl.handle.net/1992/70987
- Palabra clave:
- Deep Reinforcement Learning
Machine Learning
VideoGames
FightGames
Discrete Spaces
Constrained Resources
RL Agent Policies
Ingeniería
- Rights
- openAccess
- License
- https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf