Výsledky vyhľadávania - Population evolution multi-agent double delay deep deterministic policy gradient algorithm

Robust Adaptive Fractional-Order PID Controller Design for High-Power DC-DC Dual Active Bridge Converter Enhanced Using Multi-Agent Deep Deterministic Policy Gradient Algorithm for Electric Vehicles.

Autori: Ghamari, Seyyed Morteza Habibi, Daryoush Aziz, Asma

Zdroj: Energies (19961073); Jun2025, Vol. 18 Issue 12, p3046, 34p

Predmety: PID controllers, REINFORCEMENT learning, VOLTAGE control, POLITICAL succession, ELECTRIC vehicles

Distributed auto disturbances rejection resilient control of permanent magnetic maglev trains based on the optimized deep deterministic policy gradient algorithm.

Autori: Guo, Zhen‐yu Li, Zhong‐qi

Zdroj: IET Control Theory & Applications (Wiley-Blackwell); Jul2024, Vol. 18 Issue 11, p1383-1397, 15p

Predmety: MAGNETIC levitation vehicles, MAGNETIC control, PARTICLE swarm optimization, ALGORITHMS, UNCERTAIN systems

Orbital Multi-Player Pursuit-Evasion Game with Deep Reinforcement Learning.

Autori: Li, Zhen-yu Chen, Si Zhou, Chenghong a ďalší

Zdroj: Journal of the Astronautical Sciences; Feb2025, Vol. 72 Issue 1, p1-29, 29p

Predmety: REINFORCEMENT learning, DEEP reinforcement learning, MULTIPLAYER games, MACHINE learning, ALGORITHMS

A Data-Driven Framework for Flood Mitigation: Transformer-Based Damage Prediction and Reinforcement Learning for Reservoir Operations.

Autori: Tofighi, Soheyla Gurbuz, Faruk Mantilla, Ricardo a ďalší

Zdroj: Water (20734441); Oct2025, Vol. 17 Issue 20, p3024, 37p

Predmety: TRANSFORMER models, REINFORCEMENT learning, WATER storage, RISK assessment, HYDRAULIC models, EMPIRICAL research, FLOOD damage prevention

Geografický termín: UNITED States

An Intelligent Algorithm for Solving Weapon-Target Assignment Problem: DDPG-DNPE Algorithm.

Autori: Tengda Li Gang Wang Qiang Fu a ďalší

Zdroj: Computers, Materials & Continua; 2023, Vol. 76 Issue 3, p3499-3522, 24p

Predmety: DEEP reinforcement learning, ASSIGNMENT problems (Programming), REINFORCEMENT learning, MACHINE learning, ALGORITHMS, AIR defenses, INTELLIGENT networks

Leak Management in Water Distribution Networks Through Deep Reinforcement Learning: A Review.

Autori: Javed, Awais Wu, Wenyan Sun, Quanbin a ďalší

Zdroj: Water (20734441); Jul2025, Vol. 17 Issue 13, p1928, 30p

Predmety: REINFORCEMENT learning, LEAK detection, RESOURCE allocation, MACHINE learning, AQUEDUCTS, ARTIFICIAL intelligence, LEAKAGE, WATER conservation

Distributed deep reinforcement learning-based gas supply system coordination management method for solid oxide fuel cell.

Autori: Li, Jiawen Cui, Haoyang Jiang, Wei

Zdroj: Engineering Applications of Artificial Intelligence. Apr2023, Vol. 120, pN.PAG-N.PAG. 1p.

Predmety: *SOLID oxide fuel cells, *REINFORCEMENT learning, *MACHINE learning, *BURNUP (Nuclear chemistry), *CONCEPT mapping

Learning-Based Multi-Channel Access in 5G and Beyond Networks With Fast Time-Varying Channels.

Autori: Wang, Shaoyang Lv, Tiejun Zhang, Xuewei a ďalší

Zdroj: IEEE Transactions on Vehicular Technology; May2020, Vol. 69 Issue 5, p5203-5218, 16p

Predmety: REINFORCEMENT learning, TIME-varying networks, 5G networks, RECURRENT neural networks, MACHINE learning, RIVER channels, DEEP learning

Combining multi-agent deep deterministic policy gradient and rerouting technique to improve traffic network performance under mixed traffic conditions.

Autori: Trinh, Hung Tuan Bae, Sang-Hoon Tran, Duy Quang

Zdroj: Simulation; Oct2024, Vol. 100 Issue 10, p1033-1051, 19p

Predmety: DEEP reinforcement learning, REINFORCEMENT learning, TRAFFIC flow, NETWORK performance, TRAFFIC engineering

FAMASO: fog-adaptive multi-agent scheduling optimization.

Autori: Nagabushnam, Ganesan Kim, Kyong Hoon Choi, Yundo

Zdroj: Cluster Computing; 2025, Vol. 28 Issue 8, p1-38, 38p

Multi-strategy coupled active traffic management based on multi-agent reinforcement learning.

Autori: Zhang, Haoyu Zhong, Lixian Chen, Xiqun

Zdroj: Journal of Control & Decision; Nov2025, Vol. 12 Issue 6, p960-975, 16p

Predmety: HIGHWAY capacity, REINFORCEMENT learning, TRAFFIC engineering, QUANTITATIVE research, INTELLIGENT transportation systems, VEHICULAR ad hoc networks

UAV-asisted IoT network framework with hybrid deep reinforcement and federated learning.

Autori: Andreou, Andreas Mavromoustakis, Constandinos X. Markakis, Evangelos a ďalší

Zdroj: Scientific Reports; 10/23/2025, Vol. 15 Issue 1, p1-15, 15p

Predmety: INTERNET of things, FEDERATED learning, DRONE aircraft, NETWORK performance, RESOURCE allocation, BIT rate, REINFORCEMENT learning

Voltage Control for DC Microgrids: A Review and Comparative Evaluation of Deep Reinforcement Learning.

Autori: Muhammad, Sharafadeen Obeid, Hussein Hammou, Abdelilah a ďalší

Zdroj: Energies (19961073); Nov2025, Vol. 18 Issue 21, p5706, 33p

Predmety: VOLTAGE control, MICROGRIDS, REINFORCEMENT learning, MATHEMATICAL optimization, SIMULATION methods & models, EMPIRICAL research

Adaptive multi-objective reinforcement learning with interpretable visualization for integrated RAS management across growth cycles.

Autori: Alnemari, Ashwaq M. Elmessery, Wael M. Abdallah, Said Elshahat a ďalší

Zdroj: Aquaculture International; Dec2025, Vol. 33 Issue 7, p1-47, 47p

Multi-objective optimization enabling CFRP energy-efficient milling based on deep reinforcement learning.

Autori: Zhang, Meihang Zhang, Hua Yan, Wei a ďalší

Zdroj: Applied Intelligence; Dec2024, Vol. 54 Issue 23, p12531-12557, 27p

Predmety: REINFORCEMENT learning, DEEP reinforcement learning, OPTIMIZATION algorithms, CONSUMPTION (Economics), ENERGY consumption

Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement Learning.

Autori: Zhang, Chongli Lv, Tiejun Huang, Pingmu a ďalší

Zdroj: Sensors (14248220); Aug2023, Vol. 23 Issue 15, p6822, 20p

Predmety: BANDWIDTH allocation, DEEP reinforcement learning, REINFORCEMENT learning, CO-channel interference, GENETIC algorithms, QUALITY of service

A Cascaded Multi-Agent Reinforcement Learning-Based Resource Allocation for Cellular-V2X Vehicular Platooning Networks.

Autori: Narayanasamy, Iswarya Rajamanickam, Venkateswari

Zdroj: Sensors (14248220); Sep2024, Vol. 24 Issue 17, p5658, 25p

Predmety: RADIO resource management, INTELLIGENT transportation systems, ESTIMATION bias, EDGE computing, RESOURCE allocation

Image-Based Multi-Agent Reinforcement Learning for Demand–Capacity Balancing.

Autori: Mas-Pujol, Sergi Salamí, Esther Pastor, Enric

Zdroj: Aerospace (MDPI Publishing); Oct2022, Vol. 9 Issue 10, pN.PAG-N.PAG, 19p

Predmety: REINFORCEMENT learning, AIR traffic control, TRAFFIC flow, AIR traffic capacity, AIR flow, CONVOLUTIONAL neural networks

Where Reinforcement Learning Meets Process Control: Review and Guidelines.

Autori: Faria, Ruan de Rezende Capron, Bruno Didier Olivier Secchi, Argimiro Resende a ďalší

Zdroj: Processes; Nov2022, Vol. 10 Issue 11, p2311, 31p

Predmety: REINFORCEMENT learning, ARTIFICIAL neural networks, BATCH processing, PROCESS optimization

Improving coordination in small-scale multi-agent deep reinforcement learning through memory-driven communication.

Autori: Pesce, Emanuele Montana, Giovanni

Zdroj: Machine Learning; Sep2020, Vol. 109 Issue 9/10, p1727-1747, 21p

Predmety: DEEP learning, REINFORCEMENT learning, MACHINE learning, COMPUTER storage devices, ARTIFICIAL neural networks, COMMUNICATION patterns