  The utilization of integrated sensing and communication (ISAC) technology has
the potential to enhance the communication performance of road side units
(RSUs) through the active sensing of target vehicles. Furthermore, installing a
simultaneous transmitting and reflecting surface (STARS) on the target vehicle
can provide an extra boost to the reflection of the echo signal, thereby
improving the communication quality for in-vehicle users. However, the design
of this target-mounted STARS system exhibits significant challenges, such as
limited information sharing and distributed STARS control. In this paper, we
propose an end-to-end multi-agent deep reinforcement learning (MADRL) framework
to tackle the challenges of joint sensing and communication optimization in the
considered target-mounted STARS assisted vehicle networks. By deploying agents
on both RSU and vehicle, the MADRL framework enables RSU and vehicle to perform
beam prediction and STARS pre-configuration using their respective local
information. To ensure efficient and stable learning for continuous
decision-making, we employ the multi-agent soft actor critic (MASAC) algorithm
and the multi-agent proximal policy optimization (MAPPO) algorithm on the
proposed MADRL framework. Extensive experimental results confirm the
effectiveness of our proposed MADRL framework in improving both sensing and
communication performance through the utilization of target-mounted STARS.
Finally, we conduct a comparative analysis and comparison of the two proposed
algorithms under various environmental conditions.
