Treffer 241 - 260 von 2.056

AI-Driven Optimization of Classroom Seating: A Machine Learning Approach to Enhancing Student Performance, Collaboration, and Engagement.
Lekhi, Sushil ; Kumar, Vikash ; Priyanshu ; et al.
Journal of Neonatal Surgery. 2025 Supplement, Vol. 14, p53-68. 16p.

PyBrain.
Schaul, Tom ; Bayer, Justin ; Wierstra, Daan ; et al.
Journal of Machine Learning Research. 2/1/2010, Vol. 11 Issue 2, p743-746. 4p. 1 Diagram.

Blockchain technology for digital twin security in smart grids using interpretable generalized additive neural networks.
Suggala, Ravi Kumar ; Kumar, Jaydip ; Jain, Prateek ; et al.
Peer-to-Peer Networking & Applications; Aug2025, Vol. 18 Issue 4, p1-17, 17p

5G Routing Interfered Environment
Gahtan, Barak

A deep reinforcement learning-based scheduling framework for real-time workflows in the cloud environment.
Pan, Jiahui ; Wei, Yi
Expert Systems with Applications. Dec2024:Part D, Vol. 255, pN.PAG-N.PAG. 1p.

Autonomous Underwater Vehicle Path Planning Method of Soft Actor–Critic Based on Game Training.
Wang, Zhuo ; Lu, Hao ; Qin, Hongde ; et al.
Journal of Marine Science & Engineering; Dec2022, Vol. 10 Issue 12, p2018, 22p

A unified benchmark for deep reinforcement learning-based energy management: Novel training ideas with the unweighted reward.
Chen, Jiaxin ; Tang, Xiaolin ; Yang, Kai
Energy. Oct2024, Vol. 307, pN.PAG-N.PAG. 1p.

Multi-Task Reinforcement Learning: From Single-Agent to Multi-Agent Systems
Trang, Matthew Luu

Sinergym – A virtual testbed for building energy optimization with Reinforcement Learning.
Campoy-Nieves, Alejandro ; Manjavacas, Antonio ; Jiménez-Raboso, Javier ; et al.
Energy & Buildings. Jan2025, Vol. 327, pN.PAG-N.PAG. 1p.

Analysis and Design of Wind Turbine Monitoring System Based on Edge Computing.
Xiaoju Yin ; Yuhan Mu ; Bo Li ; et al.
EAI Endorsed Transactions on the Energy Web; 2024, Vol. 11 Issue 1, p1-7, 7p

Tecniche di Reinforcement Learning per il supporto della QoS in reti SD-WAN
BORGIANNI, LUCA ; Giordano, Stefano ; Adami, Davide
http://etd.adm.unipi.it/theses/available/etd-09132022-113157/.

Optimal control of HVAC systems through active disturbance rejection control-assisted reinforcement learning.
Cui, Can ; Xue, Jiahui ; Liu, Lanjun
Energy. May2025, Vol. 323, pN.PAG-N.PAG. 1p.

Optimized Design with Artificial Intelligence Quantum Dot White Mini LED Backlight Module Development.
Lee, Tzu-Yi ; Huang, Wei-Ta ; Chen, Jo-Hsiang ; et al.
Crystals (2073-4352); Oct2023, Vol. 13 Issue 10, p1411, 13p

Joint optimization for temperature and humidity independent control system based on multi-agent reinforcement learning with cooperative mechanisms.
Liu, Shuo ; Liu, Xiaohua ; Zhang, Tao ; et al.
Applied Energy. Dec2024, Vol. 375, pN.PAG-N.PAG. 1p.

Python语言在水文水资源领域中的应用与展望. (Chinese)
姜秋香 ; 郭伟鹏 ; 王子龙 ; et al.
Journal of Computer Engineering & Applications; 5/1/2023, Vol. 59 Issue 9, p46-58, 13p

Aprendizaje por refuerzo para control de sistemas dinámicos
Díaz Latorre, Andrés Steven ; López Sotelo, Jesús Alfonso
Universidad Autónoma de Occidente
Repositorio Institucional UAO
Barto, A. G. Sutton, R. S. Anderson, C. W. (2013). Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problems. (IEEE Transactions on Systems, Man, and Cybernetics). Recuperado de http://www.derongliu.org/adp/adp-cdrom/Barto1983.pdf Boada, M. J. Boada, B. López, V. (2015). Algoritmo de aprendizaje por refuerzo continúo para el control de un sistema de suspensión semi – activa. Revista: Iberoamericana de Ingeniería Mecánica. Chandra, A. (2018). Perceptron: The Artificial Neuron (An Essential Upgrade To The McCulloch-Pitts Neuron). Recuperado de https://towardsdatascience.com/perceptron-the-artificial-neuron-4d8c70d5cc8d Cuaya, G. (2015). Procesos de decisión de Markov aplicados en la locomoción de robots hexápodos. (Tesis de maestria) Tonantzintla. México. Recuperado de https://inaoe.repositorioinstitucional.mx/jspui/bitstream/1009/588/1/CuayaSG.pdf Covantec. (2014). Ventajas y desventajas. Recuperado de https://entrenamiento-python-basico.readthedocs.io/es/latest/leccion1/ventajas_desventajas.html#ventajas García, E. O. (2015). Aprendizaje por refuerzo mediante transferencia de conocimiento cualitativo. Recuperado de http://ccc.inaoep.mx/~jemc/blog/wp-content/uploads/2016/09/tesisOmar.pdf Github. (s.f). Openai/gym. Recuperado de: https://github.com/openai/gym/wiki/CartPole-v0 Gym.openai. (s.f). Getting Started with Gym. Recuperado de https://gym.openai.com/docs/ Gym. (s.f). CartPole-v0. Recuperado de https://gym.openai.com/envs/Cart-v0/ Gym. (s.f). MountainCar-v0. Recuperado de https://gym.openai.com/envs/MountainCar-v0/ Gym. (s.f). Pendulum-v0. Recuperado de https://gym.openai.com/envs/Pendulum-v0/ Hassabis, D. (2016). AlphaGo: using machine learning to master the ancient game of Go. Recuperado de https://blog.google/technology/ai/alphago-machine-learning-game-go/ Jaderberg, M. Wojciech, M. Czarnecki. Dunning, I. Marris, L. Lever, G. Garcia, A, Beattie, C. Rabinowitz, C. Morcos, A. Ruderman, A. Sonnerat, N. Green, T. Deason, L. Leibo, J. Silver, D. Hassabis, D. Kavukcuoglu, K. Graepel, T. (2019). Human-level performance in 3D multiplayer games with population-based reinforcement learning. Recuperado de https://science.sciencemag.org/content/364/6443/859 Lopez, R. (2015). Q-learning: Aprendizaje automático por refuerzo. Recuperado de https://rubenlopezg.wordpress.com/2015/05/12/q-learning-aprendizaje-automatico-por-refuerzo/ McCulloch, W. Pitts, W .(1943). A logical calculus of the the ideas immanet in nervous activity. Bulletin of mathematical biology, Vol 52, Recuperado de: https://link.springer.com/article/10.1007%2FBF02459570 McDonal, C. (2018). Solving multiarmed bandits: A comparison of epsilon-greedy and Thompson sampling. Recuperado de https://towardsdatascience.com/solving-multiarmed-bandits-a-comparison-of-epsilon-greedy-and-thompson-sampling-d97167ca9a50 Mnih, V. Kavukcuoglu, K. Silver, D. Graves, A. Antonoglou, L. Wierstra, D. Riedmiller, M. (2013). Playing Atari with Deep Reinforcement Learning. Recuperado de https://arxiv.org/pdf/1312.5602.pdf Moor, A. W. (1990). Efficient memory-based Learning for robot control. Recuperado de: https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-209.pdf Moor, J. (2003). The Turing test: The elusive standard of artificial intelligence. EE.UU: Science & Business Media. Nieto, J. (2018). La Inteligencia Artificial del Huawei P20 Pro, ¿cómo te afecta en el día a día? Recuperado de https://andro4all.com/huawei_ia/como-funciona-inteligencia-artificial-huawei-p20-pro Nº 171: IA Grafos-Aprendizaje por Refuerzo 03 (Activo, Q-Learning). (2017)Recuperado de https://www.youtube.com/watch?v=ZoRMKs8XLSA Ortego, D. (2017).Qué es Tensorflow? Recuperado de https://openwebinars.net/blog/que-es-tensorflow/ Pastor, J. (2017). AlphaGo aplasta al mejor jugador del mundo de Go, la inteligencia artificial es imbatible. Recuperado de https://www.xataka.com/robotica-e-ia/alphago-aplasta-al-mejor-jugador-del-mundo-de-go-la-inteligencia-artificial-es-imbatible Printista, A. M, Errecalde. M. L, Montoya, C. I. (2000). Una implementación paralela del algoritmo Q-Learning basada en un esquema de comunicación con caché. San Luis, Argentina. Recuperado de http://sedici.unlp.edu.ar/bitstream/handle/10915/23363/Documento_completo.pdf?sequence=1 RSTOPUR. Disponible en: https://rstopup.com/es-posible-la-creacion-de-un-nuevo-gimnasio-medio-ambiente-en-openai.html Ruiz, S. Hernández, B. (2014). Procesos de decisión de Markovy microescenarios para navegacióny evasión de colisiones para multitudes. Research in Computing Science. Recuperado de http://www.rcs.cic.ipn.mx/rcs/2014_74/Procesos%20de%20decision%20de%20Markov%20y%20microescenarios%20para%20navegacion%20y%20evasion%20de%20colisiones.pdf Simonini, T. (2018). An intro to Advantage Actor Critic methods: let’s play Sonic the Hedgehog!. Recuperado de https://www.freecodecamp.org/news/an-intro-to-advantage-actor-critic-methods-lets-play-sonic-the-hedgehog-86d6240171d/ Siembro, G. C. (2007) Procesos de decisión de Markov aplicados a la locomoción de robots hexápodos. Recuperado de http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/588 Sutton, R. S. Barto, A. G. (1998). Reinforcement learning: An introduction. Sutton, R. S. Barto, A. G. (1998). Introduction to reinforcement learning. Londres, Inglaterra. Recuperado de http://incompleteideas.net/book/bookdraft2017nov5.pdf Tensorflow. (s.f). An end-to-end open source machine learning platform. Recuperado de https://www.tensorflow.org/ Parra, S. (2013). La emergencia del buen juego en un tablero de damas de 1950 https://www.xatakaciencia.com/computacion/la-emergencia-del-buen-juego-en-un-tablero-de-damas-de-1950 Yoon, C. (s.f). Understanding Actor Critic Methods and A2C. Recuperado de https://towardsdatascience.com/understanding-actor-critic-methods-931b97b6df3f

ARLO: A framework for Automated Reinforcement Learning.
Mussi, Marco ; Lombarda, Davide ; Metelli, Alberto Maria ; et al.
Expert Systems with Applications. Aug2023, Vol. 224, pN.PAG-N.PAG. 1p.

An Introduction to Machine Learning in Quantitative Finance: by Hao Ni, Xin Dong, Jinsong Zheng and Guangxi Yu, World Scientific Europe Ltd (2021). E-Book. ISBN 9781786349644.
Reis, Gonçalo dos ; Strange, Calum
Quantitative Finance; Dec 2021, Vol. 21 Issue 12, p2005-2006, 2p

Automated solutions for CNN model acceleration on mobile platforms.
Liu, Yuhao ; Ma, Yanhua
Microelectronics Journal. Jun2025, Vol. 160, pN.PAG-N.PAG. 1p.

A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons.
Sun, Yushan ; Luo, Xiaokun ; Ran, Xiangrui ; et al.
Journal of Marine Science & Engineering; Mar2021, Vol. 9 Issue 3, p252, 1p