Search Results - Distributed multi-agent deep deterministic policy gradient algorithm*

3DPG: Distributed Deep Deterministic Policy Gradient Algorithms for Networked Multi-Agent Systems

Authors: Redder, Adrian Ramaswamy, Arunselvan Karl, Holger

Index Terms: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Multiagent Systems, text

URL: http://arxiv.org/abs/2201.00570

A Dynamic Deceptive Defense Framework for Zero-Day Attacks in IIoT: Integrating Stackelberg Game and Multi-Agent Distributed Deep Deterministic Policy Gradient

Authors: Shigen Shen Xiaojun Ji Yimeng Liu

Source: Computers, Materials & Continua ; ISSN: 1546-2218 (Print) ; ISSN: 1546-2226 (Online) ; Volume 85 ; Issue 2

Subject Terms: Industrial internet of things, zero-day attacks, Stackelberg game, distributed deep deterministic policy gradient, defensive spoofing, dynamic defense

File Description: application/pdf

Relation: https://doi.org/10.32604/cmc.2025.069332

Availability: https://doi.org/10.32604/cmc.2025.069332

ERA-MADDPG: An Elastic Routing Algorithm Based on Multi-Agent Deep Deterministic Policy Gradient in SDN.

Authors: Huang, Wanwei Liu, Hongchang Li, Yingying et al.

Source: Future Internet; Jul2025, Vol. 17 Issue 7, p291, 20p

Subject Terms: SOFTWARE-defined networking, REINFORCEMENT learning, CONVOLUTIONAL neural networks, ADAPTIVE control systems, ROUTING algorithms, ADAPTIVE routing (Computer network management)

Distributed Deep Deterministic Policy Gradient for Power Allocation Control in D2D-Based V2V Communications

Authors: Khoi Khac Nguyen Trung Q. Duong Ngo Anh Vien et al.

Source: IEEE Access, Vol 7, Pp 164533-164543 (2019)
Nguyen, K K, Duong, T Q, Vien, N A, Le-Khac, N-A & Nguyen, L D 2019, 'Distributed Deep Deterministic Policy Gradient for Power Allocation Control in D2D-Based V2V Communications', IEEE Access, vol. 7, pp. 164533-164543. https://doi.org/10.1109/ACCESS.2019.2952411

Subject Terms: Artificial intelligence, Device-to-Device Communication, Flexibility (engineering), Wireless Energy Harvesting and Information Transfer, name=SDG 7 - Affordable and Clean Energy, 02 engineering and technology, name=General Materials Science, name=General Computer Science, Engineering, Quality of service, Reinforcement learning, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, name=General Engineering, Electrical and Electronic Engineering, Resource allocation, Computer network, multi-agent deep reinforcement learning, Non-cooperative D2D communication, Statistics, Next Generation 5G Wireless Networks, power allocation, Intelligent Reflecting Surfaces in Wireless Communications, Computer science, and deep deterministic policy gradient (DDPG), Distributed computing, name=SDG 11 - Sustainable Cities and Communities, TK1-9971, D2D-based V2V communications, Physical Sciences, Electrical engineering. Electronics. Nuclear engineering, Mathematics

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Access URL: https://ieeexplore.ieee.org/ielx7/6287639/8600701/08894348.pdf
https://doaj.org/article/df3daab963154d1aa51d6faf43d94dc9
https://pure.qub.ac.uk/en/publications/distributed-deep-deterministic-policy-gradient-for-power-allocati
https://ieeexplore.ieee.org/document/8894348
https://dblp.uni-trier.de/db/journals/access/access7.html#NguyenDVLN19a
https://pureadmin.qub.ac.uk/ws/files/195379913/08894348.pdf
https://pure.qub.ac.uk/en/publications/e594ac54-7e6e-41a5-a3f5-642efe013cf2

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

Authors: Guo, Zhen‐yu Li, Zhong‐qi

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

Subject Terms: *MAGNETIC levitation vehicles, *MAGNETIC control, *PARTICLE swarm optimization, *ALGORITHMS, *UNCERTAIN systems

Multi-agent reinforcement learning based optimal energy sensing threshold control in distributed cognitive radio networks with directional antenna

Authors: Thi Thu Hien Pham Wonjong Noh Sungrae Cho

Source: ICT Express, Vol 10, Iss 3, Pp 472-478 (2024)

Subject Terms: Directional antennas, Reinforcement learning (RL), Multi-agent deep deterministic policy gradient (MADDPG), Cooperative spectrum sensing (CSS), 0202 electrical engineering, electronic engineering, information engineering, Cognitive radio networks (CRNs), Information technology, 0102 computer and information sciences, 02 engineering and technology, T58.5-58.64, 01 natural sciences

Access URL: https://doaj.org/article/6de8f73506c04ee6873c90ed9030213d

RS-MADDPG: Routing Strategy Based on Multi-Agent Deep Deterministic Policy Gradient for Differentiated QoS Services.

Authors: Kuang, Shi Zheng, Jinyu Liang, Shilin et al.

Source: Future Internet; Sep2025, Vol. 17 Issue 9, p393, 20p

Subject Terms: QUALITY of service, ADAPTIVE routing (Computer network management), ROUTING algorithms, SOFTWARE-defined networking, MULTIAGENT systems, QUANTITATIVE research, REINFORCEMENT learning

基于改进多智能体深度确定策略梯度算法的 配电网无功协调优化调度: 79 95 23. (Chinese)

Alternate Title: Reactive power coordination optimization scheduling of distribution network based on improved multi-agent deep deterministic policy gradient algorithm. (English)

Authors: 窦 迅 邓叶航 王顺江 et al.

Source: Electric Power Automation Equipment / Dianli Zidonghua Shebei; Aug2025, Vol. 45 Issue 8, p79-95, 23p

Subject Terms: PARTICLE swarm optimization, ELECTRICAL load, REINFORCEMENT learning, DEEP reinforcement learning, REWARD (Psychology)

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.

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

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

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

Faddeer: a deep multi-agent reinforcement learning-based scheduling algorithm for aperiodic tasks in heterogeneous fog computing networks.

Authors: Nagabushnam, Ganesan Kim, Kyong Hoon

Source: Cluster Computing; Oct2025, Vol. 28 Issue 6, p1-37, 37p

Subject Terms: REINFORCEMENT learning, DEEP reinforcement learning, PARTICLE swarm optimization, ARTIFICIAL intelligence, INFORMATION storage & retrieval systems, DEEP learning

Flash-Attention-Enhanced Multi-Agent Deep Deterministic Policy Gradient for Mobile Edge Computing in Digital Twin-Powered Internet of Things.

Authors: Gao, Yuzhe Yuan, Xiaoming Wang, Songyu et al.

Source: Mathematics (2227-7390); Jul2025, Vol. 13 Issue 13, p2164, 21p

Subject Terms: EDGE computing, DIGITAL twin, MULTIAGENT systems, REINFORCEMENT learning, ATTENTION, RESOURCE allocation, INTERNET of things

A deep reinforcement learning based approach for dynamic distributed blocking flowshop scheduling with job insertions

Authors: Xueyan Sun Birgit Vogel‐Heuser Fandi Bi et al.

Source: IET Collaborative Intelligent Manufacturing, Vol 4, Iss 3, Pp 166-180 (2022)

Subject Terms: Technological innovations. Automation, 0209 industrial biotechnology, deep reinforcement learning, HD45-45.2, Manufactures, 0202 electrical engineering, electronic engineering, information engineering, distributed blocking flowshop scheduling problem, 02 engineering and technology, dynamic scheduling, multi‐agent deep deterministic policy gradient, TS1-2301, ddc, job insertions

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Access URL: https://doaj.org/article/9c3d591dc17941abaa888b7c4a37b613
https://mediatum.ub.tum.de/1772736

Distributed Drive Autonomous Vehicle Trajectory Tracking Control Based on Multi-Agent Deep Reinforcement Learning

Authors: Yalei Liu Weiping Ding Mingliang Yang et al.

Source: Mathematics, Vol 12, Iss 11, p 1614 (2024)

Subject Terms: distributed drive autonomous vehicles, trajectory tracking, multi-agent deep reinforcement learning, deep deterministic policy gradient, deep Q-network, Mathematics, QA1-939

File Description: electronic resource

Relation: https://www.mdpi.com/2227-7390/12/11/1614; https://doaj.org/toc/2227-7390

Access URL: https://doaj.org/article/0f3b3713594d428b95971d71141486c5

Research on Game Confrontation of Unmanned Surface Vehicles Swarm Based on Multi-Agent Deep Reinforcement Learning

Authors: Changdong YU Xinyang LIU Cong CHEN et al.

Source: 水下无人系统学报, Vol 32, Iss 1, Pp 79-86 (2024)

Subject Terms: unmanned surface vehicle swarm, multi-agent deep deterministic policy gradient algorithm, deep reinforcement learning, intelligent decision-making, game confrontation, Naval architecture. Shipbuilding. Marine engineering, VM1-989

File Description: electronic resource

Relation: https://doaj.org/toc/2096-3920

Access URL: https://doaj.org/article/018c9e6c89c14515a342b69917ebb237

Dual experience replay enhanced deep deterministic policy gradient for efficient continuous data sampling.

Authors: Mohd Aris, Teh Noranis Chen, Ningning Mustapha, Norwati et al.

Source: PLoS ONE; 11/11/2025, Vol. 20 Issue 11, p1-18, 18p

Subject Terms: REINFORCEMENT learning, SAMPLING (Process), SAMPLING methods

Fault‐resilient control of parallel PV inverters using multi‐agent twin‐delayed deep deterministic policy gradient approach.

Authors: Malik, Azra Haque, Ahteshamul Kurukuru, V. S. Bharath et al.

Source: International Journal of Circuit Theory & Applications; Jul2024, Vol. 52 Issue 7, p3230-3254, 25p

Subject Terms: DEEP reinforcement learning, PHOTOVOLTAIC power systems, FAULT diagnosis, ELECTRONIC systems, DISTRIBUTED power generation

A manipulator control method based on deep deterministic policy gradient with parameter noise.

Authors: Zhang, Haifei Xu, Jian Lei, Liting et al.

Source: Neural Computing & Applications; Feb2025, Vol. 37 Issue 4, p1729-1739, 11p

Subject Terms: DEEP reinforcement learning, NOISE control, DEBUGGING, NOISE, ALGORITHMS, REINFORCEMENT learning

An intelligent MIMO run-to-run controller for semiconductor manufacturing processes based on an enhanced twin-delayed deep deterministic policy gradient algorithm.

Authors: Ma, Zhu Chen, Yonglin Pan, Tianhong

Source: Applied Intelligence; Jul2025, Vol. 55 Issue 10, p1-25, 25p

Subject Terms: DEEP reinforcement learning, REINFORCEMENT learning, ARTIFICIAL intelligence, MANUFACTURING processes, INTELLIGENT control systems, SEMICONDUCTOR manufacturing

Efficient Delay-Sensitive Task Offloading to Fog Computing with Multi-Agent Twin Delayed Deep Deterministic Policy Gradient.

Authors: Ali, Endris Mohammed Lemma, Frezewd Srinivasagan, Ramasamy et al.

Source: Electronics (2079-9292); Jun2025, Vol. 14 Issue 11, p2169, 26p

Subject Terms: DEEP reinforcement learning, MACHINE learning, RESOURCE allocation, INTERNET of things, ENERGY consumption, REINFORCEMENT learning

Adaptive Load Balancing In Sdns Using A Fuzzy-Driven Twin Delayed Deep Deterministic Policy Gradient Algorithm.

Authors: Kulkarni, Prerita Doohan, Nitika Vats

Source: International Journal of Environmental Sciences (2229-7359); 2025 Special Issue, Vol. 11, p2171-2181, 11p

Subject Terms: LOAD balancing (Computer networks), SOFTWARE-defined networking, REINFORCEMENT learning, FUZZY logic, RESOURCE allocation