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...

Full description

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