Design and implementation of a Reinforcement Learning framework for iOS devices

Pavesi, Alessandro (2022) Design and implementation of a Reinforcement Learning framework for iOS devices. [Laurea magistrale], Università di Bologna, Corso di Studio in Artificial intelligence [LM-DM270]
Documenti full-text disponibili:
[img] Documento PDF (Thesis)
Disponibile con Licenza: Creative Commons: Attribuzione - Non commerciale - Non opere derivate 4.0 (CC BY-NC-ND 4.0)

Download (949kB)


Reinforcement Learning is an increasingly popular area of Artificial Intelligence. The applications of this learning paradigm are many, but its application in mobile computing is in its infancy. This study aims to provide an overview of current Reinforcement Learning applications on mobile devices, as well as to introduce a new framework for iOS devices: Swift-RL Lib. This new Swift package allows developers to easily support and integrate two of the most common RL algorithms, Q-Learning and Deep Q-Network, in a fully customizable environment. All processes are performed on the device, without any need for remote computation. The framework was tested in different settings and evaluated through several use cases. Through an in-depth performance analysis, we show that the platform provides effective and efficient support for Reinforcement Learning for mobile applications.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Pavesi, Alessandro
Relatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
Reinforcement Learning,resource-constrained devices,iOS devices,on-device machine learning
Data di discussione della Tesi
22 Marzo 2022

Altri metadati

Statistica sui download

Gestione del documento: Visualizza il documento