DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAV

• Main Authors: MAO, Xiao, WU, Guohua, FAN, Mingfeng, CAO, Zhiguang, PEDRYCZ, Witold
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Deep reinforcement learning; divide and conquer-based framework; interactive training; multi-UAV task scheduling; Operations Research, Systems Engineering and Industrial Engineering; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/8698; https://ink.library.smu.edu.sg/context/sis_research/article/9701/viewcontent/DL_DRL_av.pdf
• Institution: Singapore Management University

Exploiting unmanned aerial vehicles (UAVs) to execute tasks is gaining growing popularity recently. To address the underlying task scheduling problem, conventional exact and heuristic algorithms encounter challenges such as rapidly increasing computation time and heavy reliance on domain knowledge,...