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Machine Learning for intelligent avatars in Virtual Reality simulations: Analysis of Reinforcement Learning techniques

Nicolo' Chiapello

Machine Learning for intelligent avatars in Virtual Reality simulations: Analysis of Reinforcement Learning techniques.

Rel. Fabrizio Lamberti, Lia Morra. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2020

Abstract:

Recent advances in Deep Reinforcement Learning (RL) allowed driving the avatar in increasingly realistic and complex environments, even Virtual Reality (VR) simulations. The granted wide degrees of freedom and the immersivity of the human in the virtual space, require the interaction with believable Non-Player Characters (NPCs) with non-scripted behaviors and able to adapt to contingent changes. This work focuses on the creation of ML-driven intelligent avatars able to behave an interact with a human in Virtual Reality simulations. Their behavior is decided by a neural network able to perceive the surrounding environment and to perform an action accordingly. This approach allows completing the assigned procedure while reacting to external stimuli, adapting to the human choices, and increasing the variance of the NPC behaviors. In particular are analyzed and compared different RL techniques, such as Curriculum Learning (progressive task complexity) and Imitative Learning (mimicking the proposed human solutions).

Relatori: Fabrizio Lamberti, Lia Morra
Anno accademico: 2019/20
Tipo di pubblicazione: Elettronica
Numero di pagine: 137
Informazioni aggiuntive: Tesi secretata. Fulltext non presente
Soggetti:
Corso di laurea: Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering)
Classe di laurea: Nuovo ordinamento > Laurea magistrale > LM-32 - INGEGNERIA INFORMATICA
Aziende collaboratrici: NON SPECIFICATO
URI: http://webthesis.biblio.polito.it/id/eprint/15322
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