  In this paper, we consider the maximization of the secrecy rate in multiple
unmanned aerial vehicles (UAV) rate-splitting multiple access (RSMA) network. A
joint beamforming, rate allocation, and UAV trajectory optimization problem is
formulated which is nonconvex. Hence, the problem is transformed into a Markov
decision problem and a novel multiagent deep reinforcement learning (DRL)
framework is designed. The proposed framework (named DUN-DRL) combines deep
unfolding to design beamforming and rate allocation, data-driven to design the
UAV trajectory, and deep deterministic policy gradient (DDPG) for the learning
procedure. The proposed DUN-DRL have shown great performance and outperformed
other DRL-based methods in the literature.
