  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.
