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Artykuły w czasopismach na temat "Apprentissage par renforcement causal"
Griffon, L., M. Chennaoui, D. Leger i M. Strauss. "Apprentissage par renforcement dans la narcolepsie de type 1". Médecine du Sommeil 15, nr 1 (marzec 2018): 60. http://dx.doi.org/10.1016/j.msom.2018.01.164.
Pełny tekst źródłaGarcia, Pascal. "Exploration guidée en apprentissage par renforcement. Connaissancesa prioriet relaxation de contraintes". Revue d'intelligence artificielle 20, nr 2-3 (1.06.2006): 235–75. http://dx.doi.org/10.3166/ria.20.235-275.
Pełny tekst źródłaDegris, Thomas, Olivier Sigaud i Pierre-Henri Wuillemin. "Apprentissage par renforcement factorisé pour le comportement de personnages non joueurs". Revue d'intelligence artificielle 23, nr 2-3 (13.05.2009): 221–51. http://dx.doi.org/10.3166/ria.23.221-251.
Pełny tekst źródłaHost, Shirley, i Nicolas Sabouret. "Apprentissage par renforcement d'actes de communication dans un système multi-agent". Revue d'intelligence artificielle 24, nr 2 (17.04.2010): 159–88. http://dx.doi.org/10.3166/ria.24.159-188.
Pełny tekst źródłaCHIALI, Ramzi. "Le texte littéraire comme référentiel préférentiel dans le renforcement de la compétence interculturelle en contexte institutionnel. Réflexion et dynamique didactique." Revue plurilingue : Études des Langues, Littératures et Cultures 7, nr 1 (14.07.2023): 70–78. http://dx.doi.org/10.46325/ellic.v7i1.99.
Pełny tekst źródłaAltintas, Gulsun, i Isabelle Royer. "Renforcement de la résilience par un apprentissage post-crise : une étude longitudinale sur deux périodes de turbulence". M@n@gement 12, nr 4 (2009): 266. http://dx.doi.org/10.3917/mana.124.0266.
Pełny tekst źródłaDutech, Alain, i Manuel Samuelides. "Apprentissage par renforcement pour les processus décisionnels de Markov partiellement observés Apprendre une extension sélective du passé". Revue d'intelligence artificielle 17, nr 4 (1.08.2003): 559–89. http://dx.doi.org/10.3166/ria.17.559-589.
Pełny tekst źródłaBOUCHET, N., L. FRENILLOT, M. DELAHAYE, M. GAILLARD, P. MESTHE, E. ESCOURROU i L. GIMENEZ. "GESTION DES EMOTIONS VECUES PAR LES ETUDIANTS EN 3E CYCLE DE MEDECINE GENERALE DE TOULOUSE AU COURS DE LA PRISE EN CHARGE DES PATIENTS : ETUDE QUALITATIVE". EXERCER 34, nr 192 (1.04.2023): 184–90. http://dx.doi.org/10.56746/exercer.2023.192.184.
Pełny tekst źródłaLaurent, Guillaume J., i Emmanuel Piat. "Apprentissage par renforcement dans le cadre des processus décisionnels de Markov factorisés observables dans le désordre. Etude expérimentale du Q-Learning parallèle appliqué aux problèmes du labyrinthe et du New York Driving". Revue d'intelligence artificielle 20, nr 2-3 (1.06.2006): 275–310. http://dx.doi.org/10.3166/ria.20.275-310.
Pełny tekst źródłaZossou, Espérance, Seth Graham-Acquaah, John Manful, Simplice D. Vodouhe i Rigobert C. Tossou. "Les petits exploitants agricoles à l’école inclusive : cas de l’apprentissage collectif par la vidéo et la radio sur la post-récolte du riz local au Bénin". International Journal of Biological and Chemical Sciences 15, nr 4 (19.11.2021): 1678–97. http://dx.doi.org/10.4314/ijbcs.v15i4.29.
Pełny tekst źródłaRozprawy doktorskie na temat "Apprentissage par renforcement causal"
Théro, Héloïse. "Contrôle, agentivité et apprentissage par renforcement". Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE028/document.
Pełny tekst źródłaSense of agency or subjective control can be defined by the feeling that we control our actions, and through them effects in the outside world. This cluster of experiences depend on the ability to learn action-outcome contingencies and a more classical algorithm to model this originates in the field of human reinforcementlearning. In this PhD thesis, we used the cognitive modeling approach to investigate further the interaction between perceived control and reinforcement learning. First, we saw that participants undergoing a reinforcement-learning task experienced higher agency; this influence of reinforcement learning on agency comes as no surprise, because reinforcement learning relies on linking a voluntary action and its outcome. But our results also suggest that agency influences reinforcement learning in two ways. We found that people learn actionoutcome contingencies based on a default assumption: their actions make a difference to the world. Finally, we also found that the mere fact of choosing freely shapes the learning processes following that decision. Our general conclusion is that agency and reinforcement learning, two fundamental fields of human psychology, are deeply intertwined. Contrary to machines, humans do care about being in control, or about making the right choice, and this results in integrating information in a one-sided way
Tournaire, Thomas. "Model-based reinforcement learning for dynamic resource allocation in cloud environments". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS004.
Pełny tekst źródłaThe emergence of new technologies (Internet of Things, smart cities, autonomous vehicles, health, industrial automation, ...) requires efficient resource allocation to satisfy the demand. These new offers are compatible with new 5G network infrastructure since it can provide low latency and reliability. However, these new needs require high computational power to fulfill the demand, implying more energy consumption in particular in cloud infrastructures and more particularly in data centers. Therefore, it is critical to find new solutions that can satisfy these needs still reducing the power usage of resources in cloud environments. In this thesis we propose and compare new AI solutions (Reinforcement Learning) to orchestrate virtual resources in virtual network environments such that performances are guaranteed and operational costs are minimised. We consider queuing systems as a model for clouds IaaS infrastructures and bring learning methodologies to efficiently allocate the right number of resources for the users.Our objective is to minimise a cost function considering performance costs and operational costs. We go through different types of reinforcement learning algorithms (from model-free to relational model-based) to learn the best policy. Reinforcement learning is concerned with how a software agent ought to take actions in an environment to maximise some cumulative reward. We first develop queuing model of a cloud system with one physical node hosting several virtual resources. On this first part we assume the agent perfectly knows the model (dynamics of the environment and the cost function), giving him the opportunity to perform dynamic programming methods for optimal policy computation. Since the model is known in this part, we also concentrate on the properties of the optimal policies, which are threshold-based and hysteresis-based rules. This allows us to integrate the structural property of the policies into MDP algorithms. After providing a concrete cloud model with exponential arrivals with real intensities and energy data for cloud provider, we compare in this first approach efficiency and time computation of MDP algorithms against heuristics built on top of the queuing Markov Chain stationary distributions.In a second part we consider that the agent does not have access to the model of the environment and concentrate our work with reinforcement learning techniques, especially model-based reinforcement learning. We first develop model-based reinforcement learning methods where the agent can re-use its experience replay to update its value function. We also consider MDP online techniques where the autonomous agent approximates environment model to perform dynamic programming. This part is evaluated in a larger network environment with two physical nodes in tandem and we assess convergence time and accuracy of different reinforcement learning methods, mainly model-based techniques versus the state-of-the-art model-free methods (e.g. Q-Learning).The last part focuses on model-based reinforcement learning techniques with relational structure between environment variables. As these tandem networks have structural properties due to their infrastructure shape, we investigate factored and causal approaches built-in reinforcement learning methods to integrate this information. We provide the autonomous agent with a relational knowledge of the environment where it can understand how variables are related to each other. The main goal is to accelerate convergence by: first having a more compact representation with factorisation where we devise a factored MDP online algorithm that we evaluate and compare with model-free and model-based reinforcement learning algorithms; second integrating causal and counterfactual reasoning that can tackle environments with partial observations and unobserved confounders
Zimmer, Matthieu. "Apprentissage par renforcement développemental". Thesis, Université de Lorraine, 2018. http://www.theses.fr/2018LORR0008/document.
Pełny tekst źródłaReinforcement learning allows an agent to learn a behavior that has never been previously defined by humans. The agent discovers the environment and the different consequences of its actions through its interaction: it learns from its own experience, without having pre-established knowledge of the goals or effects of its actions. This thesis tackles how deep learning can help reinforcement learning to handle continuous spaces and environments with many degrees of freedom in order to solve problems closer to reality. Indeed, neural networks have a good scalability and representativeness. They make possible to approximate functions on continuous spaces and allow a developmental approach, because they require little a priori knowledge on the domain. We seek to reduce the amount of necessary interaction of the agent to achieve acceptable behavior. To do so, we proposed the Neural Fitted Actor-Critic framework that defines several data efficient actor-critic algorithms. We examine how the agent can fully exploit the transitions generated by previous behaviors by integrating off-policy data into the proposed framework. Finally, we study how the agent can learn faster by taking advantage of the development of his body, in particular, by proceeding with a gradual increase in the dimensionality of its sensorimotor space
Kozlova, Olga. "Apprentissage par renforcement hiérarchique et factorisé". Phd thesis, Université Pierre et Marie Curie - Paris VI, 2010. http://tel.archives-ouvertes.fr/tel-00632968.
Pełny tekst źródłaFilippi, Sarah. "Stratégies optimistes en apprentissage par renforcement". Phd thesis, Ecole nationale supérieure des telecommunications - ENST, 2010. http://tel.archives-ouvertes.fr/tel-00551401.
Pełny tekst źródłaMunos, Rémi. "Apprentissage par renforcement, étude du cas continu". Paris, EHESS, 1997. http://www.theses.fr/1997EHESA021.
Pełny tekst źródłaLesner, Boris. "Planification et apprentissage par renforcement avec modèles d'actions compacts". Caen, 2011. http://www.theses.fr/2011CAEN2074.
Pełny tekst źródłaWe study Markovian Decision Processes represented with Probabilistic STRIPS action models. A first part of our work is about solving those processes in a compact way. To that end we propose two algorithms. A first one based on propositional formula manipulation allows to obtain approximate solutions in tractable propositional fragments such as Horn and 2-CNF. The second algorithm solves exactly and efficiently problems represented in PPDDL using a new notion of extended value functions. The second part is about learning such action models. We propose different approaches to solve the problem of ambiguous observations occurring while learning. Firstly, a heuristic method based on Linear Programming gives good results in practice yet without theoretical guarantees. We next describe a learning algorithm in the ``Know What It Knows'' framework. This approach gives strong theoretical guarantees on the quality of the learned models as well on the sample complexity. These two approaches are then put into a Reinforcement Learning setting to allow an empirical evaluation of their respective performances
Maillard, Odalric-Ambrym. "APPRENTISSAGE SÉQUENTIEL : Bandits, Statistique et Renforcement". Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2011. http://tel.archives-ouvertes.fr/tel-00845410.
Pełny tekst źródłaKlein, Édouard. "Contributions à l'apprentissage par renforcement inverse". Thesis, Université de Lorraine, 2013. http://www.theses.fr/2013LORR0185/document.
Pełny tekst źródłaThis thesis, "Contributions à l'apprentissage par renforcement inverse", brings three major contributions to the community. The first one is a method for estimating the feature expectation, a quantity involved in most of state-of-the-art approaches which were thus extended to a batch off-policy setting. The second major contribution is an Inverse Reinforcement Learning algorithm, structured classification for inverse reinforcement learning (SCIRL), which relaxes a standard constraint in the field, the repeated solving of a Markov Decision Process, by introducing the temporal structure (using the feature expectation) of this process into a structured margin classification algorithm. The afferent theoritical guarantee and the good empirical performance it exhibited allowed it to be presentend in a good international conference: NIPS. Finally, the third contribution is cascaded supervised learning for inverse reinforcement learning (CSI) a method consisting in learning the expert's behavior via a supervised learning approach, and then introducing the temporal structure of the MDP via a regression involving the score function of the classifier. This method presents the same type of theoretical guarantee as SCIRL, but uses standard components for classification and regression, which makes its use simpler. This work will be presented in another good international conference: ECML
Gelly, Sylvain. "Une contribution à l'apprentissage par renforcement : application au Computer Go". Paris 11, 2007. http://www.theses.fr/2007PA112227.
Pełny tekst źródłaReinforcement Learning (RL) is at the interface of control theory, supervised and unsupervised learning, optimization and cognitive sciences. While RL addresses many objectives with major economic impact, it raises deep theoretical and practical difficulties. This thesis brings some contributions to RL, mainly on three axis. The first axis corresponds to environment modeling, i. E. Learning the transition function between two time steps. Factored approaches give an efficiently framework for the learning and use of this model. The Bayesian Networks are a tool to represent such a model, and this work brings new learning criterion, either in parametric learning (conditional probabilities) and non parametric (structure). The second axis is a study in continuous space and action RL, thanks to the dynamic programming algorithm. This analysis tackles three fundamental steps: optimization (action choice from the value function), supervised learning (regression) of the value function and choice of the learning examples (active learning). The third axis tackles the applicative domain of the game of Go, as a high dimensional discrete control problem, one of the greatest challenge in Machine Learning. The presented algorithms with their improvements made the resulting program, MoGo, win numerous international competitions, becoming for example the first go program playing at an amateur dan level on 9x9
Książki na temat "Apprentissage par renforcement causal"
Sutton, Richard S. Reinforcement learning: An introduction. Cambridge, Mass: MIT Press, 1998.
Znajdź pełny tekst źródłaOntario. Esquisse de cours 12e année: Sciences de l'activité physique pse4u cours préuniversitaire. Vanier, Ont: CFORP, 2002.
Znajdź pełny tekst źródłaOntario. Esquisse de cours 12e année: Technologie de l'information en affaires btx4e cours préemploi. Vanier, Ont: CFORP, 2002.
Znajdź pełny tekst źródłaOntario. Esquisse de cours 12e année: Études informatiques ics4m cours préuniversitaire. Vanier, Ont: CFORP, 2002.
Znajdź pełny tekst źródłaOntario. Esquisse de cours 12e année: Mathématiques de la technologie au collège mct4c cours précollégial. Vanier, Ont: CFORP, 2002.
Znajdź pełny tekst źródłaOntario. Esquisse de cours 12e année: Sciences snc4m cours préuniversitaire. Vanier, Ont: CFORP, 2002.
Znajdź pełny tekst źródłaOntario. Esquisse de cours 12e année: English eae4e cours préemploi. Vanier, Ont: CFORP, 2002.
Znajdź pełny tekst źródłaOntario. Esquisse de cours 12e année: Le Canada et le monde: une analyse géographique cgw4u cours préuniversitaire. Vanier, Ont: CFORP, 2002.
Znajdź pełny tekst źródłaOntario. Esquisse de cours 12e année: Environnement et gestion des ressources cgr4e cours préemploi. Vanier, Ont: CFORP, 2002.
Znajdź pełny tekst źródłaOntario. Esquisse de cours 12e année: Histoire de l'Occident et du monde chy4c cours précollégial. Vanier, Ont: CFORP, 2002.
Znajdź pełny tekst źródłaCzęści książek na temat "Apprentissage par renforcement causal"
Tazdaït, Tarik, i Rabia Nessah. "5. Vote et apprentissage par renforcement". W Le paradoxe du vote, 157–77. Éditions de l’École des hautes études en sciences sociales, 2013. http://dx.doi.org/10.4000/books.editionsehess.1931.
Pełny tekst źródłaBENDELLA, Mohammed Salih, i Badr BENMAMMAR. "Impact de la radio cognitive sur le green networking : approche par apprentissage par renforcement". W Gestion du niveau de service dans les environnements émergents. ISTE Group, 2020. http://dx.doi.org/10.51926/iste.9002.ch8.
Pełny tekst źródłaRaporty organizacyjne na temat "Apprentissage par renforcement causal"
Melloni, Gian. Le leadership des autorités locales en matière d'assainissement et d'hygiène : expériences et apprentissage de l'Afrique de l'Ouest. Institute of Development Studies (IDS), styczeń 2022. http://dx.doi.org/10.19088/slh.2022.002.
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