Suchergebnisse - "Deep reinforcement learning algorithm"

Federated Deep Reinforcement Learning for ENDC Optimization

Autoren: Adrian Martin Isabel de-la-Bandera Adriano Mendo et al.

Quelle: RIUMA. Repositorio Institucional de la Universidad de Málaga
Universidad de Málaga

Schlagwörter: Optimization, Handover, Learning agent, Optimal network, Deep reinforcement learning algorithm, Network capacity, Federated learning, Heterogeneous network, Deep neural network, Small step, Deep reinforcement learning agent, Key performance indicators, Aprendizaje automático (Inteligencia artificial), Reinforcement learning algorithm, Training, Heuristic algorithms, Training phase, Training cell, Individual agency, Neighboring cells, Long term evolution, Deep reinforcement learning, 5 G NSA, Telecomunicaciones, Multi party computation, Hysteresis, Cell clusters, Rest of the cells, Reinforcement learning agent, Event B 1, Cellular networks, Deep learning, RAN Optimization, Rate of network, Throughput, 5 G Mobile communication, User equipment, Mobile edge computing

Zugangs-URL: https://hdl.handle.net/10630/38565

Research on the implementation and effectiveness evaluation of deep reinforcement learning algorithms for portfolio optimisation

Autoren: Gao Yunxiang Tang Bangying

Quelle: Discover Artificial Intelligence, Vol 5, Iss 1, Pp 1-12 (2025)

Schlagwörter: Deep reinforcement learning algorithm, Portfolio optimisation, Effectiveness evaluation, A2C, PPO, Computational linguistics. Natural language processing, P98-98.5, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2731-0809

Zugangs-URL: https://doaj.org/article/ea0dd1c5fe3c4c01b7c171ce10c6ea3d

A hybrid MARL clustering framework for real time open pit mine truck scheduling

Autoren: Xiaolei Xiang Wei Lin Danqi Li

Quelle: Scientific Reports, Vol 15, Iss 1, Pp 1-23 (2025)

Schlagwörter: Multi-agent deep reinforcement learning algorithm, QMIX, Gaussian mixture model, Open-pit mining, Truck scheduling, Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/f7ac25e2b3aa4d10b60e96df41633cce

Adaptive Multi-Scale Bidirectional TD3 Algorithm for Layout Optimization of UAV–Base Station Coordination in Mountainous Areas

Autoren: Leyi Wang Jianbo Tan Hanbo Gong et al.

Quelle: Drones, Vol 9, Iss 11, p 805 (2025)

Schlagwörter: base station layout, deep reinforcement learning algorithm, differential evolution algorithm, UAV, Motor vehicles. Aeronautics. Astronautics, TL1-4050

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2504-446X/9/11/805; https://doaj.org/toc/2504-446X

Zugangs-URL: https://doaj.org/article/1cadac0407b4410a9d6f0758a9d21d44

Robust Deep Reinforcement Learning Algorithm for VNF-FG Embedding

Autoren: Bouroudi, Abdelmounaim Outtagarts, Abdelkader Hadjadj-Aoul, Yassine et al.

Weitere Verfasser: Bouroudi, Abdelmounaim Outtagarts, Abdelkader Hadjadj-Aoul, Yassine et al.

Quelle: 2022 IEEE 47th Conference on Local Computer Networks (LCN). :351-354

Schlagwörter: Deep Reinforcement Learning, [INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI], B5G/6G, Algorithm Selection, 0202 electrical engineering, electronic engineering, information engineering, Virtual Network Embedding Deep Reinforcement Learning Algorithm Selection Stationary bandit B5G/6G, 02 engineering and technology, Stationary bandit, Virtual Network Embedding

Dateibeschreibung: application/pdf

Zugangs-URL: https://inria.hal.science/hal-03911352v1
https://inria.hal.science/hal-03911352v1/document
https://doi.org/10.1109/lcn53696.2022.9843650

Low-carbon economic dispatch strategy for integrated electrical and gas system with GCCP based on multi-agent deep reinforcement learning

Autoren: Wentao Feng Bingyan Deng Ziwen Zhang et al.

Quelle: Frontiers in Energy Research, Vol 12 (2024)

Schlagwörter: GCCP, 13. Climate action, integrated electrical and gas system, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, carbon emission, 02 engineering and technology, multi-agent deep reinforcement learning algorithm, low-carbon, 7. Clean energy, General Works, 12. Responsible consumption

Zugangs-URL: https://doaj.org/article/e2bfbac0d64f4f6eb72dfddab8d3ad7d

ADAS-RL: Safety learning approach for stable autonomous driving

Autoren: Dongsu Lee Minhae Kwon

Quelle: ICT Express, Vol 8, Iss 3, Pp 479-483 (2022)

Schlagwörter: Autonomous vehicle, 0209 industrial biotechnology, Deep reinforcement learning algorithm, 0502 economics and business, 05 social sciences, Information technology, 02 engineering and technology, T58.5-58.64, Markov decision process

Zugangs-URL: https://doaj.org/article/01eb636b438140e29f04628d4b470752

An Applied Deep Reinforcement Learning Approach to Control Active Networked Microgrids in Smart Cities with Multi-Level Participation of Battery Energy Storage System and Electric Vehicles

Autoren: Reza Sepehrzad Amir Saman Godazi Langeroudi Amin Khodadadi et al.

Quelle: Sepehrzad, R, Godazi Langeroudi, A S, Khodadadi, A, Adinehpour, S, Al-Durra, A & Anvari-Moghaddam, A 2024, 'An Applied Deep Reinforcement Learning Approach to Control Active Networked Microgrids in Smart Cities with Multi-Level Participation of Battery Energy Storage System and Electric Vehicles', Sustainable Cities and Society, vol. 107, 105352, pp. 1-21. https://doi.org/10.1016/j.scs.2024.105352

Schlagwörter: name=SDG 7 - Affordable and Clean Energy, Electric vehicles, Deep reinforcement learning algorithm, 11. Sustainability, Energy management, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, name=SDG 17 - Partnerships for the Goals, name=SDG 9 - Industry, Innovation, and Infrastructure, 7. Clean energy, Networked microgrid

Dateibeschreibung: application/pdf

Zugangs-URL: https://vbn.aau.dk/da/publications/946bd639-b3d2-457c-b769-dde5e83fe3f1
http://www.scopus.com/inward/record.url?scp=85191004487&partnerID=8YFLogxK
https://doi.org/10.1016/j.scs.2024.105352
https://vbn.aau.dk/ws/files/766592800/1-s2.0-S221067072400180X-main.pdf

FlexSim-Simulated PCB Assembly Line Optimization Using Deep Q-Network

Autoren: Jinhao Du Jabir Mumtaz Wenxi Zhao et al.

Quelle: Engineering Proceedings, Vol 75, Iss 1, p 34 (2024)

Schlagwörter: printed circuit board, FlexSim simulation, PCB assembly line, deep reinforcement learning algorithm, Engineering machinery, tools, and implements, TA213-215

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2673-4591/75/1/34; https://doaj.org/toc/2673-4591

Zugangs-URL: https://doaj.org/article/6026ca6b98214645808761998518604b

An Applied Deep Reinforcement Learning Approach to Control Active Networked Microgrids in Smart Cities with Multi-Level Participation of Battery Energy Storage System and Electric Vehicles

Autoren: Sepehrzad, Reza Godazi Langeroudi, Amir Saman Khodadadi, Amin et al.

Quelle: Sepehrzad, R, Godazi Langeroudi, A S, Khodadadi, A, Adinehpour, S, Al-Durra, A & Anvari-Moghaddam, A 2024, 'An Applied Deep Reinforcement Learning Approach to Control Active Networked Microgrids in Smart Cities with Multi-Level Participation of Battery Energy Storage System and Electric Vehicles', Sustainable Cities and Society, vol. 107, 105352, pp. 1-21. https://doi.org/10.1016/j.scs.2024.105352

Schlagwörter: Networked microgrid, Electric vehicles, Energy management, Deep reinforcement learning algorithm, /dk/atira/pure/sustainabledevelopmentgoals/affordable_and_clean_energy, name=SDG 7 - Affordable and Clean Energy, /dk/atira/pure/sustainabledevelopmentgoals/industry_innovation_and_infrastructure, name=SDG 9 - Industry, Innovation, and Infrastructure, /dk/atira/pure/sustainabledevelopmentgoals/partnerships, name=SDG 17 - Partnerships for the Goals

Dateibeschreibung: application/pdf

Relation: info:eu-repo/semantics/altIdentifier/pissn/2210-6707

Verfügbarkeit: https://vbn.aau.dk/da/publications/946bd639-b3d2-457c-b769-dde5e83fe3f1
https://doi.org/10.1016/j.scs.2024.105352
https://vbn.aau.dk/ws/files/766592800/1-s2.0-S221067072400180X-main.pdf
https://www.scopus.com/pages/publications/85191004487

Optimizing Maritime Energy Efficiency: A Machine Learning Approach Using Deep Reinforcement Learning for EEXI and CII Compliance

Autoren: Mohammed H. Alshareef Ayman F. Alghanmi

Quelle: Sustainability ; Volume 16 ; Issue 23 ; Pages: 10534

Schlagwörter: alternative fuels in shipping, CII ratings, deep reinforcement learning algorithm, EEXI compliance, optimization, maritime energy efficiency

Geographisches Schlagwort: agris

Dateibeschreibung: application/pdf

Relation: Sustainable Transportation; https://dx.doi.org/10.3390/su162310534

Verfügbarkeit: https://doi.org/10.3390/su162310534

Multi‐robot path planning based on a deep reinforcement learning DQN algorithm

Autoren: Yang Yang Peng Lingling Li Juntao

Quelle: CAAI Transactions on Intelligence Technology (2020)

Schlagwörter: multirobot path planning, volume-based technology, multi-robot systems, robot path-planning problem, path-planning problems, unmanned warehouse dispatching system, 02 engineering and technology, warehouse picking operation, scheduling system algorithm, improved dqn algorithm converges, QA76.75-76.765, deep q-network algorithm, mobile robots, Computational linguistics. Natural language processing, 0202 electrical engineering, electronic engineering, information engineering, learning (artificial intelligence), q-learning algorithm, handling robot, Computer software, classic deep reinforcement learning algorithm, P98-98.5, path planning, algorithmic process

Zugangs-URL: https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/trit.2020.0024
https://doaj.org/article/f65a13d5958a4533aa5e0fd31f5e1ada
https://dblp.uni-trier.de/db/journals/caaitrit/caaitrit5.html#YangJL20
https://digital-library.theiet.org/content/journals/10.1049/trit.2020.0024
https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/trit.2020.0024

Modeling & implementation of DRLA based partially shaded solar system integration with 3-ϕ conventional grid using constant current controller

Autoren: Radhika Guntupalli M. Sudhakaran P. Ajay-D-Vimal raj

Quelle: Heliyon, Vol 8, Iss 6, Pp e09669- (2022)

Schlagwörter: Maximum power point tracking (MPPT), Deep reinforcement learning algorithm (DRLA), Deep learning algorithm (DLA), Reinforcement learning algorithm (RLA), Deep deterministic policy gradient (DDPG), Partial shading conditions (PSC), Science (General), Q1-390, Social sciences (General), H1-99

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2405844022009574; https://doaj.org/toc/2405-8440

Zugangs-URL: https://doaj.org/article/1e74ea69c6af4213864d7313f745d958

Deep Reinforcement Learning Algorithm for the Design of Robust Attitude Control System for Flexible Structure Spacecraft / 深層強化学習を用いた柔軟構造衛星のロバスト姿勢制御系設計

Autoren: Hiroyuki Takano Isao Yamaguchi Ryo Endo et al.

Quelle: Proceedings of the Japan Joint Automatic Control Conference. 2022, :1434

Energy Management in Electric Vehicles Using Improved Swarm Optimized Deep Reinforcement Learning Algorithm

Autoren: Jawale, M.A. Pawar, A.B. Korde, S.K. et al.

Quelle: Journal of Nano- and Electronic Physics. 15:03004-1

Schlagwörter: energy management, 13. Climate action, енергоменеджмент, improved swarm optimized deep reinforcement learning algorithm (IS-DRLA), електричні транспортні засоби, 7. Clean energy, удосконалений алгоритм глибокого підсилення навчання (IS-DRLA), electric vehicles

Dateibeschreibung: application/pdf

Zugangs-URL: https://essuir.sumdu.edu.ua/handle/123456789/92374

Deep Catan

Autoren: Driss, Brahim Cazenave, Tristan

Schlagwörter: Multiplayer board game, Convolutional neural network, Deep reinforcement learning algorithm, Expert Iteration, Alpha Zero, Recherche opérationnelle

Time: 003

Relation: Applications of Evolutionary Computation : EvoApplications 2022; Springer International Publishing; Berlin Heidelberg; 2022; SPAIN; non; https://basepub.dauphine.psl.eu/handle/123456789/24585

Verfügbarkeit: https://basepub.dauphine.psl.eu/handle/123456789/24585
https://doi.org/10.1007/978-3-031-02462-7_32

Energy Management in Electric Vehicles Using Improved Swarm Optimized Deep Reinforcement Learning Algorithm ; Управління енергією в електричних транспортних засобах з використанням вдосконаленого алгоритму глибокого підсилення

Autoren: Jawale, M.A. Pawar, A.B. Korde, S.K. et al.

Schlagwörter: енергоменеджмент, електричні транспортні засоби, удосконалений алгоритм глибокого підсилення навчання (IS-DRLA), energy management, electric vehicles, improved swarm optimized deep reinforcement learning algorithm (IS-DRLA)

Dateibeschreibung: application/pdf

Relation: https://essuir.sumdu.edu.ua/handle/123456789/92374

Verfügbarkeit: https://essuir.sumdu.edu.ua/handle/123456789/92374

Deep Q networks-based optimization of emergency resource scheduling for urban public health events.

Autoren: Zhao X Wang G

Quelle: Neural computing & applications [Neural Comput Appl] 2023; Vol. 35 (12), pp. 8823-8832. Date of Electronic Publication: 2022 Aug 24.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Springer International Country of Publication: England NLM ID: 9313239 Publication Model: Print-Electronic Cited Medium: Print ISSN: 0941-0643 (Print) Linking ISSN: 09410643 NLM ISO Abbreviation: Neural Comput Appl Subsets: PubMed not MEDLINE