Campardo, Giorgia
(2024)
A Comparative Analysis of Reinforcement Learning Algorithms in a Hybrid Learning and Optimization Framework.
[Laurea magistrale], Università di Bologna, Corso di Studio in
Artificial intelligence [LM-DM270]
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Abstract
The integration of Machine Learning (ML) and Constrained Optimization (CO) represents a promising avenue for enhancing decision-making capabilities in complex, uncertain environments. This thesis empirically evaluates the UNIFY framework, a novel approach that synergistically combines the predictive power of ML with the strategic prowess of CO. Focusing on the Energy Management System and Set Multi-cover with stochastic coverages problems, in this work we carry out a comparative analysis on the performance, efficiency, and scalability of four RL algorithms - A2C, PPO, TD3, and SAC - within the UNIFY framework. Empirical results reveal the strengths and limitations of these algorithms, highlighting SAC's superior sample efficiency and the benefits of training on multiple instances for improved model generalization. These findings underscore the potential of integrating ML and CO through RL, offering valuable insights for the development of advanced decision-making systems in various real-world applications.
Abstract
The integration of Machine Learning (ML) and Constrained Optimization (CO) represents a promising avenue for enhancing decision-making capabilities in complex, uncertain environments. This thesis empirically evaluates the UNIFY framework, a novel approach that synergistically combines the predictive power of ML with the strategic prowess of CO. Focusing on the Energy Management System and Set Multi-cover with stochastic coverages problems, in this work we carry out a comparative analysis on the performance, efficiency, and scalability of four RL algorithms - A2C, PPO, TD3, and SAC - within the UNIFY framework. Empirical results reveal the strengths and limitations of these algorithms, highlighting SAC's superior sample efficiency and the benefits of training on multiple instances for improved model generalization. These findings underscore the potential of integrating ML and CO through RL, offering valuable insights for the development of advanced decision-making systems in various real-world applications.
Tipologia del documento
Tesi di laurea
(Laurea magistrale)
Autore della tesi
Campardo, Giorgia
Relatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
Artificial Intelligence,Reinforcement Learning,Constrained Optimization
Data di discussione della Tesi
19 Marzo 2024
URI
Altri metadati
Tipologia del documento
Tesi di laurea
(NON SPECIFICATO)
Autore della tesi
Campardo, Giorgia
Relatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
Artificial Intelligence,Reinforcement Learning,Constrained Optimization
Data di discussione della Tesi
19 Marzo 2024
URI
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