  Reconfigurable intelligent surface (RIS) has recently gained popularity as a
promising solution for improving the signal transmission quality of wireless
communications with less hardware cost and energy consumption. This letter
offers a novel deep reinforcement learning (DRL) algorithm based on a
location-aware imitation environment for the joint beamforming design in an
RIS-aided mmWave multiple-input multiple-output system. Specifically, we design
a neural network to imitate the transmission environment based on the geometric
relationship between the user's location and the mmWave channel. Following
this, a novel DRL-based method is developed that interacts with the imitation
environment using the easily available location information. Finally,
simulation results demonstrate that the proposed DRL-based algorithm provides
more robust performance without excessive interaction overhead compared to the
existing DRL-based approaches.
