Suchergebnisse - deep deterministic policy gradient algorithm, Ant Colony Optimization

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

Autoren: Xiangcheng Li Zhaokai Ruan Yang Ou et al.

Quelle: Electronics. 13:4071

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

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

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

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

Logistics-involved service composition in a dynamic cloud manufacturing environment: A DDPG-based approach

Autoren: Liu, Yongkui Liang, Huagang Xiao, Yingying et al.

Quelle: Robotics and Computer-Integrated Manufacturing. 76:102323

Schlagwörter: Cloud manufacturing, service composition, deep reinforcement learning, deep deterministic policy gradient algorithm, Ant Colony Optimization

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-313518

Joint task offloading and resource allocation for multi-user collaborative mobile edge computing.

Autoren: An, Xiaobei Li, Yanjun Chen, Yuzhe et al.

Quelle: Computer Networks. Aug2024, Vol. 250, pN.PAG-N.PAG. 1p.

Schlagwörter: *MOBILE computing, *RESOURCE allocation, *NONLINEAR programming, EDGE computing, ANT algorithms

Improved Performance for PMSM Sensorless Control Based on the LADRC Controller, ESO-Type Observer, DO-Type Observer, and RL-TD3 Agent.

Autoren: Nicola, Claudiu-Ionel Nicola, Marcel

Quelle: Mathematics (2227-7390); Aug2023, Vol. 11 Issue 15, p3324, 25p

Schlagwörter: REINFORCEMENT learning, SENSORLESS control systems, DEEP reinforcement learning, PERMANENT magnet motors, COMPUTATIONAL intelligence, ANT algorithms

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

Autoren: Wei, Linxin Xu, Quanxing Hu, Ziyu

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

Twin-Delayed Deep Deterministic Policy Gradient Algorithm to Control a Boost Converter in a DC Microgrid.

Autoren: Muktiadji, Rifqi Firmansyah Ramli, Makbul A. M. Milyani, Ahmad H.

Quelle: Electronics (2079-9292); Jan2024, Vol. 13 Issue 2, p433, 22p

Schlagwörter: MICROGRIDS, BOOSTING algorithms, REINFORCEMENT learning, DEEP learning, ENGINEERING design, VOLTAGE control, REACTION time

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

Autoren: Qiong Huang Ying Si Haoyu Wang

Quelle: International Journal of Advanced Computer Science & Applications; Jul2024, Vol. 15 Issue 7, p387-395, 9p

Path Planning of Unmanned Aerial Vehicle in Complex Environments Based on State-Detection Twin Delayed Deep Deterministic Policy Gradient.

Autoren: Zhang, Danyang Xuan, Zhaolong Zhang, Yang et al.

Quelle: Machines; Jan2023, Vol. 11 Issue 1, p108, 18p

Schlagwörter: REINFORCEMENT learning, DRONE aircraft, AUTOMOBILE speed

Robot movement planning for obstacle avoidance using reinforcement learning.

Autoren: Schneider, Linda-Sophie Peng, Junyan Maier, Andreas

Quelle: Scientific Reports; 9/12/2025, Vol. 14 Issue 1, p1-11, 11p

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

Autoren: Tengda Li Gang Wang Qiang Fu et al.

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

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

基于KP-DDPG 的矿山运载机器人 路径规划实验平台设计. (Chinese)

Alternate Title: Design of the experimental platform for path planning of mine carrier robots based on KP-DDPG. (English)

Autoren: 杨健健 程 琪 章 腾 et al.

Quelle: Experimental Technology & Management; Jan2025, Vol. 42 Issue 1, p143-151, 9p

Joint optimization task offloading and trajectory control for unmanned-aerial-vehicle-assisted mobile edge computing.

Autoren: Xu, Fei Wang, Sen Su, Weiya et al.

Quelle: Computers & Electrical Engineering. Oct2023:Part A, Vol. 111, pN.PAG-N.PAG. 1p.

Schlagwörter: *ANT algorithms, *MOBILE computing, *EDGE computing, *DRONE aircraft, *DEEP reinforcement learning, *REINFORCEMENT learning, *REMOTELY piloted vehicles, *TRAJECTORY optimization

Optimal Design of Planar Microwave Microfluidic Sensors Based on Deep Reinforcement Learning.

Autoren: Wang, Bin-Xiao Zhao, Wen-Sheng Wang, Da-Wei et al.

Quelle: IEEE Sensors Journal; 12/15/2021, Vol. 21 Issue 24, p27441-27449, 9p

Intermittent Stop-Move Motion Planning for Dual-Arm Tomato Harvesting Robot in Greenhouse Based on Deep Reinforcement Learning.

Autoren: Li, Yajun Feng, Qingchun Zhang, Yifan et al.

Quelle: Biomimetics (2313-7673); Feb2024, Vol. 9 Issue 2, p105, 18p

Schlagwörter: DEEP reinforcement learning, REINFORCEMENT learning, TOMATO harvesting, TOMATOES, ROBOTS, GREENHOUSES, HARVESTING, GREENHOUSE plants

End-to-End AUV Local Motion Planning Method Based on Deep Reinforcement Learning.

Autoren: Lyu, Xi Sun, Yushan Wang, Lifeng et al.

Quelle: Journal of Marine Science & Engineering; Sep2023, Vol. 11 Issue 9, p1796, 32p

Schlagwörter: DEEP reinforcement learning, AUTONOMOUS underwater vehicles, REINFORCEMENT learning, PRODUCTION planning

Day‐ahead optimal dispatching of hybrid power system based on deep reinforcement learning.

Autoren: Shi, Yakun Mu, Chaoxu Hao, Yi et al.

Quelle: Cognitive Computation & Systems; Dec2022, Vol. 4 Issue 4, p351-361, 11p

Schlagwörter: REINFORCEMENT learning, HYBRID power systems, PHOTOVOLTAIC power systems, ENERGY consumption, WIND power, RENEWABLE energy sources, CONTROL (Psychology)

Geografische Kategorien: NEW England

Prioritized experience replay in path planning via multi-dimensional transition priority fusion.

Autoren: Nuo Cheng Peng Wang Guangyuan Zhang et al.

Quelle: Frontiers in Neurorobotics; 2023, p1-16, 16p

Schlagwörter: DEEP reinforcement learning, ENTROPY (Information theory)

Ant-Inspired Metaheuristic Algorithms for Combinatorial Optimization Problems in Water Resources Management.

Autoren: Bhavya, Ravinder Elango, Lakshmanan

Quelle: Water (20734441); May2023, Vol. 15 Issue 9, p1712, 19p

Schlagwörter: GROUNDWATER monitoring, WATER management, ANT algorithms, WATER distribution, METAHEURISTIC algorithms, MATHEMATICAL analysis, COASTAL zone management, CONFERENCE papers

Deep reinforcement learning-based microservice selection in mobile edge computing.

Autoren: Guo, Feiyan Tang, Bing Tang, Mingdong et al.

Quelle: Cluster Computing; Apr2023, Vol. 26 Issue 2, p1319-1335, 17p

Schlagwörter: MOBILE learning, MOBILE computing, EDGE computing, REINFORCEMENT learning, MARKOV processes