  Integrated sensing and communication (ISAC) technology is essential for
enabling the vehicular networks. However, the communication channel in this
scenario exhibits time-varying characteristics, and the potential targets may
move rapidly, creating a doubly-dynamic phenomenon. This nature poses a
challenge for real-time precoder design. While optimization-based solutions are
widely researched, they are complex and heavily rely on perfect prior
information, which is impractical in double dynamics. To address this
challenge, we propose using constrained deep reinforcement learning (CDRL) to
facilitate dynamic updates to the ISAC precoder design. Additionally, the
primal dual-deep deterministic policy gradient (PD-DDPG) and Wolpertinger
architecture are tailored to efficiently train the algorithm under complex
constraints and variable numbers of users. The proposed scheme not only adapts
to the dynamics based on observations but also leverages environmental
information to enhance performance and reduce complexity. Its superiority over
existing candidates has been validated through experiments.
