DL-DRL: A double-layer deep reinforcement learning approach for large-scale task scheduling of multi-UAV. (arXiv:2208.02447v2 [cs.LG] UPDATED)

This paper studies deep reinforcement learning (DRL) for the task scheduling
problem of multiple unmanned aerial vehicles (UAVs). Current approaches
generally use exact and heuristic algorithms to solve the problem, while the
computation time rapidly increases as the task scale grows and heuristic rules
need manual design. As a self-learning method, DRL can obtain a high-quality
solution quickly without hand-engineered rules. However, the huge decision
space makes the training of DRL models becomes unstable in situations with
large-scale tasks. In …

arxiv dl learning lg reinforcement reinforcement learning scale scheduling task scheduling