  This paper presents a new deep reinforcement learning (DRL)-based approach to
the trajectory planning and jamming rejection of an unmanned aerial vehicle
(UAV) for the Internet-of-Things (IoT) applications. Jamming can prevent timely
delivery of sensing data and reception of operation instructions. With the
assistance of a reconfigurable intelligent surface (RIS), we propose to augment
the radio environment, suppress jamming signals, and enhance the desired
signals. The UAV is designed to learn its trajectory and the RIS configuration
based solely on changes in its received data rate, using the latest deep
deterministic policy gradient (DDPG) and twin delayed DDPG (TD3) models.
Simulations show that the proposed DRL algorithms give the UAV with strong
resistance against jamming and that the TD3 algorithm exhibits faster and
smoother convergence than the DDPG algorithm, and suits better for larger RISs.
This DRL-based approach eliminates the need for knowledge of the channels
involving the RIS and jammer, thereby offering significant practical value.
