Self-Supervised Path Planning in UAV-aided Wireless Networks based on Active Inference

arXiv:2403.13827v1 Announce Type: cross
Abstract: This paper presents a novel self-supervised path-planning method for UAV-aided networks. First, we employed an optimizer to solve training examples offline and then used the resulting solutions as demonstrations from which the UAV can learn the world model to understand the environment and implicitly discover the optimizer's policy. UAV equipped with the world model can make real-time autonomous decisions and engage in online planning using active inference. During planning, UAV can score different policies based …

abstract arxiv cs.lg cs.ro eess.sp environment examples inference learn networks novel offline paper path planning solutions solve the environment training type wireless world world model