  In this paper, we investigate the problem of fast spectrum sharing in
vehicle-to-everything communication. In order to improve the spectrum
efficiency of the whole system, the spectrum of vehicle-to-infrastructure links
is reused by vehicle-to-vehicle links. To this end, we model it as a problem of
deep reinforcement learning and tackle it with proximal policy optimization. A
considerable number of interactions are often required for training an agent
with good performance, so simulation-based training is commonly used in
communication networks. Nevertheless, severe performance degradation may occur
when the agent is directly deployed in the real world, even though it can
perform well on the simulator, due to the reality gap between the simulation
and the real environments. To address this issue, we make preliminary efforts
by proposing an algorithm based on meta reinforcement learning. This algorithm
enables the agent to rapidly adapt to a new task with the knowledge extracted
from similar tasks, leading to fewer interactions and less training time.
Numerical results show that our method achieves near-optimal performance and
exhibits rapid convergence.
