Reinforcement learning in problemi di controllo del bilanciamento

Si pone come obiettivo della tesi lo studio di algoritmi di reinforcement learning capaci di istruire un agente ad interagire correttamente con gli ambienti proposti con lo scopo di risolvere i problemi presentati. Nello specifico i problemi verteranno su un argomento comune: il balancing, ovvero problemi legati all'equilibrio. Questa tesi presenta quindi due algoritmi per risolvere i problemi appena elencati: un algoritmo di Q-learning con uso di una Q-table per la memorizzazione delle componenti stato-azione e uno di Q-network in cui la Q-table viene sostituita da una rete neurale.

Gli ambienti legati ai problemi che verranno affrontati sono realizzati attraverso pyBullet, libreria per la simulazione 3D di corpi solidi che viene integrata con Gym openAI, toolkit per la programmazione in ambito machine learning che offre semplici interfacce per la costruzione di nuovi ambienti. Gestione del documento:. Reinforcement Learning in problemi di controllo del bilanciamento. Buzzoni, Michele Reinforcement Learning in problemi di controllo del bilanciamento.

Salva citazione. Dublin Core. Abstract Si pone come obiettivo della tesi lo studio di algoritmi di reinforcement learning capaci di istruire un agente ad interagire correttamente con gli ambienti proposti con lo scopo di risolvere i problemi presentati.

Tipologia del documento. Maltoni, Davide. Ingegneria e Architettura. Altri metadati Tipologia del documento. Vedi altre statistiche.I documenti in POLITesi sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Recentemente sono state sviluppate delle neuroprotesi multi-elttrodo per superare i limiti dei classici sistemi per la FES. Abstract in inglese One of the most challenging issue in post-stroke rehabilitation is the restoration of the hand grasp functions. Among all the treatment found in literature, functional electrical stimulation FES is proposed as a promising technology in this field.

In the last decades multi-electrode neuroprostheses have been developed to overcome the main limits of the classical FES systems. However, these new devices still suffer from problems in the stimulation sites identification and in the control due to the high non-linear dynamics and time variability of the controlled system, the human arm.

RL could represent a solution to this problem due to its capability of adapting to non-linear and time-varying systems: RL algorithms learn from data in a trial and error fashion and they do not strictly require a model of the environment with they interact.

reinforcement learning in problemi di controllo del bilanciamento

The aim of this thesis was to investigate the feasibility of Reinforcement Learning-based methods in the identification and control of a multi-electrode FES neuroprosthesis for the recovery of hand functions.

Anno accademico. Titolo della tesi. A reinforcement learning controller of a multi-pad electrode neuroprosthesis for grasping : a feasibility study. Abstract in italiano. Abstract in inglese.

One of the most challenging issue in post-stroke rehabilitation is the restoration of the hand grasp functions. Tipo di documento. Tesi di laurea Magistrale.Le deep Q-network, il modello actor-critic e i deep deterministic policy gradient sono esempi popolari di algoritmi.

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This website uses cookies to improve your user experience, personalize content and ads, and analyze website traffic. By continuing to use this website, you consent to our use of cookies. Deep learning. Ricerca MathWorks. Close Mobile Search. Software di prova Contattaci. Reinforcement Learning. Agenti di reinforcement learning. Creare ambienti MATLAB per il reinforcement learning - Documentazione Creare ambienti Simulink per il reinforcement learning - Documentazione Definire segnali di ricompensa per sistemi continui e discreti - Documentazione Addestrare un agente utilizzando il calcolo parallelo in Simulink - Esempio.

Esempi e applicazioni di riferimento. Ulteriori informazioni sul Reinforcement Learning. Reinforcement Learning Toolbox - Presentazione Importazione di architetture di reti neurali profonde pre-addestrate - Documentazione Distribuzione delle politiche addestrate alle GPU - Documentazione.

Come addestrare il tuo robot con il Deep Reinforcement Learning - Video Reinforcement Learning 5 Video - Serie di video Reinforcement Learning per un pendolo inverso con dati dii immagini - Video Reinforcement learning per il controllo Field-Oriented di un motore sincrono a magneti permanenti - Video.

Richiedi una versione di prova gratuita 30 giorni di prova a tua disposizione. Scarica ora. Hai domande? Consulta un esperto di deep learning. Email us.I documenti in POLITesi sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione. Inizieremo analizzando i recenti sviluppi nell'ambito del safe reinforcement learning, ovvero algoritmi con forti garanzie teoriche. Qui presenteremo algoritmi che, sfruttando un bound inferiore al guadagno atteso di una politica, sono in grado di garantire un miglioramento monotono della funzione obiettivo ad ogni iterazione.

Presenteremo tre nuovi algoritmi che sfruttano metodi a gradiente per costruire una approssimazione della frontiera di Pareto, sia continua che discreta. Partendo da questi risultati, mostreremo che gli approcci multi-obiettivo sono un modo naturale per spiegare problemi di inverse reinforcement learning con parametrizzazione lineare della funzione di rinforzo.

Inverse reinforcement learning ambisce a recuperare le informazioni che fanno agire l'agente in modo ottimo. In aggiunta, presenteremo un algoritmo GIRL che rimuove l'assunzione di parametrizzazione lineare.

Abstract in inglese This thesis is mainly based on the idea that the design of an algorithm must be supported by theoretical results. Instead of starting from an algorithm and analyzing its properties and guarantees, we will derive theoretical results and we will investigate their applicability to design practical algorithms. All the analysis will be performed in the reinforcement learning framework, i.

This thesis summarizes recent results in different reinforcement learning topics and builds on these results to provide novel algorithms with an attention to performance guarantees. The thesis is organized in 3 parts, one for each studied topic. We start investigating the recent advances in the framework of safe reinforcement learning, i.

We suggest algorithms that, by exploiting a lower bound to the expected performance gain, are able to guarantee a monotonic performance improvement overtime. The idea behind this analysis is to reduce the gap with classical control theory where the most attention is based on robustness and stability.

However, to face many real applications reinforcement learning must be able to handle problems with multiple objectives. The second part of the thesis deals with this topic. We present three new algorithms that exploit gradient methods to construct a continuous or discrete approximation of the Pareto frontier.

On the top of that, we will show that the multi-objective framework is a natural way to explain the problem of inverse reinforcement learning with linear reward parametrization. The goal of inverse reinforcement learning is to recover the motivations that make an agent to behave optimally. We will present an algorithm GIRL that removes the assumption of linear parametrization. Moreover, we will show that PGIRL is an efficient implementation of the more general nonlinear-reward algorithm GIRL in the particular case of linear reward parametrizations.

Titolo della tesi. Reinforcement learning: from theory to algorithms. Abstract in italiano. Questa tesi si basa sull'idea che i risultati teorici devono fornire la base portante su cui definire la struttura di un algoritmo.

Comparing humans with the best Reinforcement Learning algorithms

Abstract in inglese. This thesis is mainly based on the idea that the design of an algorithm must be supported by theoretical results. Tipo di documento. Tesi di dottorato. Tesi di Dottorato.Nei due metodi citati, i programmi vengono prima alimentati con dati. I giochi per computer offrono un ambiente perfetto per esplorare e comprendere il reinforcement learning. Inoltre, pongono di solito un problema o compiti complessi nelle varie sezioni del gioco che devono essere risolti. Il computer riceve delle ricompense in momenti diversi che influenzano le sue strategie.

Le righe contengono tutte le osservazioni possibili mentre le colonne riportano tutte le azioni possibili. La rappresentazione visiva funziona solo in un observation space di piccole dimensioni. Il gruppo utilizza il reinforcement learning, ad esempio, per controllare gli impianti di climatizzazione nei data center.

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Introduzione al reinforcement learning: cos’è, quando si usa?

Per capire come fanno Alexa o Siri a risponderci in modo appropriato o a darci consigli musicali personalizzati, bisogna comprendere i concetti di IA. Machine Learning e Deep Learning sono due approcci fondamentali che bisognerebbe conoscere.

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reinforcement learning in problemi di controllo del bilanciamento

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Reinforcement learning: quando le macchine imparano a pensare

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