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 on the
expected surprise, allowing it to choose among alternative futures.
Additionally, UAV can anticipate the outcomes of its actions using the world
model and assess the expected surprise in a self-supervised manner. Our method
enables quicker adaptation to new situations and better performance than
traditional RL, leading to broader generalizability.

一种用于 UAV 辅助网络的新型自监督路径规划方法，通过离线解决训练示例并使用解决方案作为演示来让 UAV 学习世界模型从而理解环境，并隐含地发现优化器的策略。具备世界模型的 UAV 可以进行实时自主决策和在线规划，通过对预期惊喜进行评分，可以选择最优策略。此方法比传统的 RL 更快适应新情境且性能更好，从而具备更广泛的泛化能力。