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Listar por palabra clave "Deep reinforcement learning"

Mostrando ítems 1-8 de 8

An Artificial Intelligence framework for air navigation based on Deep Reinforcement Learning

Navarro García, Luis (Universitat Politècnica de València, 2021-09-02)

[EN] Air traffic has been continuously rising. The expected exponential growth of unmanned traffic, like for delivery services, can result in an uncontrollable airspace that might rely on finding solutions that depend on ...
Approaching Generalized Planning using Deep Reinforcement Learning and Graph Neural Networks

Aso Mollar, Ángel (Universitat Politècnica de València, 2022-10-13)

[ES] Uno de los grandes retos de la Inteligencia Artificial ha sido y sigue siendo la capacidad de inferir soluciones dadas instancias pequeñas de un problema, o incluso subproblemas, que puedan generalizar hacia otras de ...
Deep Reinforcement Learning for Flow Control Exploits Different Physics for Increasing Reynolds Number Regimes

Varela-Martínez, Pau; Suárez Morales, Pol; Alcántara-Ávila, Francisco; Miró, Arnau; Rabault, Jean; Font, Bernat; García-Cuevas González, Luis Miguel; Lehmkuhl, Oriol; Vinuesa, Ricardo (MDPI AG, 2022-12)

[EN] The increase in emissions associated with aviation requires deeper research into novel sensing and flow-control strategies to obtain improved aerodynamic performances. In this context, data-driven methods are suitable ...
Deep Reinforcement Learning Mechanism for Dynamic Access Control in Wireless Networks Handling mMTC

Pacheco-Paramo, Diego F.; Tello-Oquendo, Luis; Pla, Vicent; Martínez Bauset, Jorge (Elsevier, 2019-11)

[EN] One important issue that needs to be addressed in order to provide effective massive deployments of IoT devices is access control. In 5G cellular networks, the Access Class Barring (ACB) method aims at increasing the ...
Federated learning: A cutting-edge survey of the latest advancements and applications

Akhtarshenas, Azim; Vahedifar, Mohammad Ali; Ayoobi, Navid; Maham, Behrouz; Alizadeh, Tohid; Ebrahimi, Sina; López-Pérez, David (Elsevier, 2024-12-01)

[EN] Robust machine learning (ML) models can be developed by leveraging large volumes of data and distributing the computational tasks across numerous devices or servers. Federated learning (FL) is a technique in the realm ...
Inteligencia Artificial con Open AI Gym y Ray RLlib para el Aprendizaje Interactivo de la Planificación de Requerimiento de Materiales

Serrano Ruiz, Julio César; Peidro Payá, David; Mula Bru, Josefa; Poler Escoto, Raúl (Editorial Universitat Politècnica de València, 2022-10-28)

[EN] Material requirements planning (MRP) is a process whose purpose is to guarantee the flow of materials into production, ensuring that each of the necessary materials is received in the required quantity and on the ...
Job shop smart manufacturing scheduling by deep reinforcement learning for Industry 4.0

Serrano Ruiz, Julio César (Universitat Politècnica de València, 2025-01-24)

[ES] El paradigma de la Industria 4.0 (I4.0) gravita en gran medida sobre el potencial de las tecnologías de la información y la comunicación (TIC) para mejorar la competitividad y sostenibilidad de las industrias. El ...
Reinforcement learning applied to production planning and control

Esteso, Ana; Peidro Payá, David; Mula, Josefa; Díaz-Madroñero Boluda, Francisco Manuel (Taylor & Francis, 2023-08-18)

[EN] The objective of this paper is to examine the use and applications of reinforcement learning (RL) techniques in the production planning and control (PPC) field addressing the following PPC areas: facility resource ...