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Titre : Machine Learning: The Basics Type de document : Guide/Manuel Auteurs : Alexander Jung, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2022 Importance : 280 p. Note générale : glossaire
arXiv:1805.05052Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage dirigé
[Termes IGN] apprentissage non-dirigé
[Termes IGN] apprentissage par renforcement
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] intelligence artificielle
[Termes IGN] modèle numériqueRésumé : (auteur) Machine learning (ML) has become a commodity in our every-day lives. We routinely ask ML empowered smartphones to suggest lovely food places or to guide us through a strange place. ML methods have also become standard tools in many fields of science and engineering. A plethora of ML applications transform human lives at unprecedented pace and scale. This book portrays ML as the combination of three basic components: data, model and loss. ML methods combine these three components within computationally efficient implementations of the basic scientific principle "trial and error". This principle consists of the continuous adaptation of a hypothesis about a phenomenon that generates data. ML methods use a hypothesis to compute predictions for future events. We believe that thinking about ML as combinations of three components given by data, model, and loss helps to navigate the steadily growing offer for ready-to-use ML methods. Our three-component picture of ML allows a unified treatment of a wide range of concepts and techniques which seem quite unrelated at first sight. The regularization effect of early stopping in iterative methods is due to the shrinking of the effective hypothesis space. Privacy-preserving ML is obtained by particular choices for the features of data points. Explainable ML methods are characterized by particular choices for the hypothesis space. To make good use of ML tools it is instrumental to understand its underlying principles at different levels of detail. On a lower level, this tutorial helps ML engineers to choose suitable methods for the application at hand. The book also offers a higher-level view on the implementation of ML methods which is typically required to manage a team of ML engineers and data scientists. Numéro de notice : 17721 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/INFORMATIQUE Nature : Manuel de cours DOI : sans En ligne : https://arxiv.org/abs/1805.05052 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100081 Simulating multi-exit evacuation using deep reinforcement learning / Dong Xu in Transactions in GIS, Vol 25 n° 3 (June 2021)
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Titre : Simulating multi-exit evacuation using deep reinforcement learning Type de document : Article/Communication Auteurs : Dong Xu, Auteur ; Xiao Huang, Auteur ; Joseph Mango, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1542-1564 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse comparative
[Termes IGN] apprentissage par renforcement
[Termes IGN] distribution spatiale
[Termes IGN] itinéraire piétionnier
[Termes IGN] modèle de simulation
[Termes IGN] réseau neuronal profondRésumé : (Auteur) Conventional simulations on multi-exit indoor evacuation focus primarily on how to determine a reasonable exit based on numerous factors in a changing environment. Results commonly include some congested and other under-utilized exits, especially with large numbers of pedestrians. We propose a multi-exit evacuation simulation based on deep reinforcement learning (DRL), referred to as the MultiExit-DRL, which involves a deep neural network (DNN) framework to facilitate state-to-action mapping. The DNN framework applies Rainbow Deep Q-Network (DQN), a DRL algorithm that integrates several advanced DQN methods, to improve data utilization and algorithm stability and further divides the action space into eight isometric directions for possible pedestrian choices. We compare MultiExit-DRL with two conventional multi-exit evacuation simulation models in three separate scenarios: varying pedestrian distribution ratios; varying exit width ratios; and varying open schedules for an exit. The results show that MultiExit-DRL presents great learning efficiency while reducing the total number of evacuation frames in all designed experiments. In addition, the integration of DRL allows pedestrians to explore other potential exits and helps determine optimal directions, leading to a high efficiency of exit utilization. Numéro de notice : A2021-466 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Numéro de périodique nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12738 Date de publication en ligne : 11/03/2021 En ligne : https://doi.org/10.1111/tgis.12738 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98085
in Transactions in GIS > Vol 25 n° 3 (June 2021) . - pp 1542-1564[article]Exploration of reinforcement learning algorithms for autonomous vehicle visual perception and control / Florence Carton (2021)
Titre : Exploration of reinforcement learning algorithms for autonomous vehicle visual perception and control Titre original : Exploration des algorithmes d'apprentissage par renforcement pour la perception et le controle d'un véhicule autonome par vision Type de document : Thèse/HDR Auteurs : Florence Carton, Auteur ; David Filliat, Directeur de thèse Editeur : Paris : Ecole Nationale Supérieure des Techniques Avancées ENSTA Année de publication : 2021 Importance : 173 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l’Institut Polytechnique de Paris, Spécialité : Informatique, Données, IALangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage par renforcement
[Termes IGN] classification dirigée
[Termes IGN] instrument embarqué
[Termes IGN] navigation autonome
[Termes IGN] reconnaissance de formes
[Termes IGN] réseau neuronal profond
[Termes IGN] robot mobile
[Termes IGN] segmentation sémantique
[Termes IGN] vision par ordinateurIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Reinforcement learning is an approach to solve a sequential decision making problem. In this formalism, an autonomous agent interacts with an environment and receives rewards based on the decisions it makes. The goal of the agent is to maximize the total amount of rewards it receives. In the reinforcement learning paradigm, the agent learns by trial and error the policy (sequence of actions) that yields the best rewards.In this thesis, we focus on its application to the perception and control of an autonomous vehicle. To stay close to human driving, only the onboard camera is used as input sensor. We focus in particular on end-to-end training, i.e. a direct mapping between information from the environment and the action chosen by the agent. However, training end-to-end reinforcement learning for autonomous driving poses some challenges: the large dimensions of the state and action spaces as well as the instability and weakness of the reinforcement learning signal to train deep neural networks.The approaches we implemented are based on the use of semantic information (image segmentation). In particular, this work explores the joint training of semantic information and navigation.We show that these methods are promising and allow to overcome some limitations. On the one hand, combining segmentation supervised learning with navigation reinforcement learning improves the performance of the agent and its ability to generalize to an unknown environment. On the other hand, it enables to train an agent that will be more robust to unexpected events and able to make decisions limiting the risks.Experiments are conducted in simulation, and numerous comparisons with state of the art methods are made. Note de contenu : 1- Introduction
2- Supervised learning and reinforcement learning background
3- State of the art
4- End-to-end autonomous driving on circuit with reinforcement learning
5- From lane following to robust conditional driving
6- Exploration of methods to reduce overfit
7- ConclusionNuméro de notice : 28325 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Informatique, Données, IA : ENSTA : 2021 DOI : sans En ligne : https://tel.hal.science/tel-03273748/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98363 Machine learning for the distributed and dynamic management of a fleet of taxis and autonomous shuttles / Tatiana Babicheva (2021)
Titre : Machine learning for the distributed and dynamic management of a fleet of taxis and autonomous shuttles Titre original : Machine Learning pour la gestion distribuée et dynamique d’une flotte de taxis et navettes autonomes Type de document : Thèse/HDR Auteurs : Tatiana Babicheva, Auteur ; Leïla Kloul, Directeur de thèse ; Dominique Barth, Directeur de thèse Editeur : Bures-sur-Yvette : Université Paris-Saclay Année de publication : 2021 Importance : 190 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université Paris-Saclay, InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] apprentissage par renforcement
[Termes IGN] autopartage
[Termes IGN] calcul d'itinéraire
[Termes IGN] méthode heuristique
[Termes IGN] navigation autonome
[Termes IGN] OpenStreetMap
[Termes IGN] optimisation (mathématiques)
[Termes IGN] réseau neuronal artificiel
[Termes IGN] réseau routier
[Termes IGN] taxi
[Termes IGN] trafic routier
[Termes IGN] trafic urbain
[Termes IGN] véhicule électrique
[Termes IGN] ville intelligenteIndex. décimale : THESE Thèses et HDR Résumé : (auteur) In this thesis are investigated methods to manage shared electric autonomous taxi urban systems under online context in which customer demands occur over time, and where vehicles are available for ride-sharing and require electric recharging management. We propose the heuristics based on problem decomposition which include road network repartition and highlighting of subproblems such as charging management, empty vehicle redistribution and dynamic ride-sharing.The set of new methods for empty vehicle redistribution is proposed, such as proactive, meaning to take into account both current demand and anticipated future demand, in contrast to reactive methods, which act based on current demand only.We provide the reinforcement learning in different levels depending on granularity of the system.We propose station-based RL model for small networks and zone-based RL model, where the agents are zones of the city obtained by partitioning, for huge ones. The complete information optimisation is provided in order to analyse the system performance a-posteriori in offline context.The evaluation of the performance of proposed methods is provided in set of road networks of different nature and size. The proposed method provides promising results outperforming the other tested methods and the real data on the taxi system performance in terms of number of satisfied passengers under fixed fleet size. Note de contenu : 1- Introduction
2- State-of-the-art
3- Modelling the electrical aTaxisystem
4- Functional architecture of aTaxi system management
5- Reinforcement learning for aTaxi system optimisation
6- Evaluation scenarii
7- Numerical evaluation of aTaxi systems
8- Conclusion and discussionNuméro de notice : 28591 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Thèse française Note de thèse : thèse de Doctorat : Informatique : Paris-Saclay : 2021 Organisme de stage : Données et Algorithmes pour une ville intelligente et durable (UVSQ) DOI : sans En ligne : https://tel.hal.science/tel-03230845/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97968
Titre : Multi-agent systems : Strategies and applications Type de document : Monographie Auteurs : Ricardo Lopez-Ruiz, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 170 p. Format : 19 x 27 cm ISBN/ISSN/EAN : 978-1-78985-394-0 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage par renforcement
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'image
[Termes IGN] réseau neuronal artificiel
[Termes IGN] système multi-agentsRésumé : (éditeur) Research on multi-agent systems is enlarging our future technical capabilities as humans and as an intelligent society. During recent years many effective applications have been implemented and are part of our daily life. These applications have agent-based models and methods as an important ingredient. Markets, finance world, robotics, medical technology, social negotiation, video games, big-data science, etc. are some of the branches where the knowledge gained through multi-agent simulations is necessary and where new software engineering tools are continuously created and tested in order to reach an effective technology transfer to impact our lives. This book brings together researchers working in several fields that cover the techniques, the challenges and the applications of multi-agent systems in a wide variety of aspects related to learning algorithms for different devices such as vehicles, robots and drones, computational optimization to reach a more efficient energy distribution in power grids and the use of social networks and decision strategies applied to the smart learning and education environments in emergent countries. We hope that this book can be useful and become a guide or reference to an audience interested in the developments and applications of multi-agent systems. Note de contenu : 1- Cooperative adaptive learning control for a group of nonholonomic UGVs by output feedback
2- Multiagent systems for 3D reconstruction applications
3- A Q-learning-based approach for simple and multi-agent systems
4- Multi-Agent systems, simulation and nanotechnology
5- Applications of multi-agent system in power system engineering
6- Architecture of a microgrid and optimal energy management system
7- Multi-agent systems based advanced energy management of smart micro-grid
8- Smart learning environment: Paradigm shift for onlint learning
9- ICT: Vehicle for educational development and social TransformationNuméro de notice : 28572 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.81766 Date de publication en ligne : 22/04/2020 En ligne : https://doi.org/10.5772/intechopen.81766 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97871 PermalinkIntelligence artificielle et jeux / Tristan Cazenave (2006)PermalinkAAMAS'05, fifth European workshop on adaptive agents and multi-agent systems, March 21 - 22, 2005, Paris, France / Eduardo Alonso (2005)Permalink