Search Results - improved multi-agent deep deterministic policy gradient algorithm

Multiple unmanned aerial vehicle coordinated strikes against ground targets based on an improved multi-agent deep deterministic policy gradient algorithm

Authors: Wei Li Xin Chen Wei Yu et al.

Source: Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering.

Subject Terms: 0203 mechanical engineering, 0103 physical sciences, 02 engineering and technology, 01 natural sciences

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: Research outputs 2022 to 2026

Index Terms: deep deterministic policy gradient, dual active bridge converter, fractional-order concept, Hardware-in-Loop, multi-agent, reinforcement learning technique, Electrical and Computer Engineering, Engineering, text

URL: https://ro.ecu.edu.au/ecuworks2022-2026/6501

Fire Evacuation Path Planning Based on Improved MADDPG (Multi-Agent Deep Deterministic Policy Gradient) Algorithm

Authors: Qiong Huang Ying Si Haoyu Wang

Source: International Journal of Advanced Computer Science and Applications. 15

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)

Improved Multi-Agent Deep Deterministic Policy Gradient for Path Planning-Based Crowd Simulation

Authors: Shangfei Zheng Hong Liu

Source: IEEE Access, Vol 7, Pp 147755-147770 (2019)

Subject Terms: Deep reinforcement learning, improved multi-agent deep deterministic policy gradient algorithm, multi-agent reinforcement learning, 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, crowd simulation for evacuation, path planning, TK1-9971

Access URL: https://ieeexplore.ieee.org/ielx7/6287639/8600701/08865095.pdf
https://doaj.org/article/f1c103a658d8479f943e594a29828bd1
https://dblp.uni-trier.de/db/journals/access/access7.html#ZhengL19
https://ieeexplore.ieee.org/abstract/document/8865095

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: Seyyed Morteza Ghamari Daryoush Habibi Asma Aziz

Source: Energies, Vol 18, Iss 12, p 3046 (2025)

Subject Terms: reinforcement learning technique, Technology, fractional-order concept, deep deterministic policy gradient, multi-agent, dual active bridge converter, Hardware-in-Loop

Access URL: https://doaj.org/article/4d16de7de27346aa8f77b5ab4879ecc3

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)

A Q-Learning-based Deep Deterministic Policy Gradient Algorithm for the Re-entrant Hybrid Flow Shop Joint Scheduling Problem with Dual-gripper.

Authors: Jiajia Wang Hongming Zhou Jian Guo et al.

Source: Engineering Letters. May2025, Vol. 33 Issue 5, p1632-1647. 16p.

Subject Terms: *FLOW shop scheduling, *FLOW shops, *MULTIAGENT systems, *PRODUCTION scheduling, *MATHEMATICAL models, *MANUFACTURING workstations

Design of Robust Adaptive Nonlinear Backstepping Controller Enhanced by Deep Deterministic Policy Gradient Algorithm for Efficient Power Converter Regulation.

Authors: Ghamari, Seyyed Morteza Aziz, Asma Ghahramani, Mehrdad

Source: Energies (19961073); Sep2025, Vol. 18 Issue 18, p4941, 27p

Subject Terms: ADAPTIVE control systems, REINFORCEMENT learning, GREY Wolf Optimizer algorithm, NONLINEAR systems, STABILITY (Mechanics), CONVERTERS (Electronics), HARDWARE-in-the-loop simulation

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

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

Optimal bidding strategy for the price-maker virtual power plant in the day-ahead market based on multi-agent twin delayed deep deterministic policy gradient algorithm.

Authors: Jiang, Yuzheng Dong, Jun Huang, Hexiang

Source: Energy. Oct2024, Vol. 306, pN.PAG-N.PAG. 1p.

Subject Terms: *OPTIMIZATION algorithms, *BIDDING strategies, *REINFORCEMENT learning, *ELECTRICITY markets, *ELECTRICITY pricing

基于多智能体深度确定策略梯度算法的 火力发电商竞价策略. (Chinese)

Alternate Title: Bidding Strategy for Thermal Power Generation Companies Based on Multi-agent Deep Deterministic Policy Gradient Algorithm. (English)

Authors: 张兴平 王腾 张馨月 et al.

Source: Electric Power; Nov2024, Vol. 57 Issue 11, p161-172, 12p

Research on the influence of multi-agent deep deterministic policy gradient algorithm key parameters in typical scenarios.

Authors: Lu, Junsong Wang, Zongsheng Pan, Kang et al.

Source: Journal of Physics: Conference Series; 2024, Vol. 2858 Issue 1, p1-12, 12p

Adaptive Deep Ant Colony Optimization–Asymmetric Strategy Network Twin Delayed Deep Deterministic Policy Gradient Algorithm: Path Planning for Mobile Robots in Dynamic Environments.

Authors: Li, Xiangcheng Ruan, Zhaokai Ou, Yang et al.

Source: Electronics (2079-9292); Oct2024, Vol. 13 Issue 20, p4071, 22p

Subject Terms: ANT algorithms, ROBOTIC path planning, MACHINE learning, DEEP learning, MOBILE learning, MOBILE robots

A Hierarchical Reinforcement Learning Framework for Multi-Agent Cooperative Maneuver Interception in Dynamic Environments.

Authors: Huang, Qinlong Luo, Yasong Liu, Zhong et al.

Source: Journal of Marine Science & Engineering; Jul2025, Vol. 13 Issue 7, p1271, 32p

Subject Terms: REINFORCEMENT learning, GRAPH neural networks, MULTIAGENT systems, RESOURCE management, DEEP learning, ROBOTIC path planning

Energy Scheduling of Hydrogen Hybrid UAV Based on Model Predictive Control and Deep Deterministic Policy Gradient Algorithm.

Authors: Li, Haitao Wang, Chenyu Yuan, Shufu et al.

Source: Algorithms; Feb2025, Vol. 18 Issue 2, p80, 20p

Subject Terms: REINFORCEMENT learning, DEEP reinforcement learning, PROTON exchange membrane fuel cells, MACHINE learning, ELECTRIC power consumption

Optimal demand response based dynamic pricing strategy via Multi-Agent Federated Twin Delayed Deep Deterministic policy gradient algorithm.

Authors: Ma, Haining Zhang, Huifeng Tian, Ding et al.

Source: Engineering Applications of Artificial Intelligence. Jul2024:Part A, Vol. 133, pN.PAG-N.PAG. 1p.

Subject Terms: *TIME-based pricing, *FEDERATED learning, *REINFORCEMENT learning, *LOAD management (Electric power), *DISTRIBUTED algorithms, *ALGORITHMS

Mobile robot path planning based on multi-experience pool deep deterministic policy gradient in unknown environment.

Authors: Wei, Linxin Xu, Quanxing Hu, Ziyu

Source: International Journal of Machine Learning & Cybernetics; Dec2024, Vol. 15 Issue 12, p5823-5837, 15p

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

Authors: Liu, Yalei Ding, Weiping Yang, Mingliang et al.

Source: Mathematics (2227-7390). Jun2024, Vol. 12 Issue 11, p1614. 21p.

Subject Terms: *DEEP reinforcement learning, *REINFORCEMENT learning, *AUTONOMOUS vehicles, *MOTOR vehicle driving, *TORQUE control, *MARKOV processes