  In this letter, we investigate the hybrid beamforming for millimeter wave
massive multiple-input multiple-output (MIMO) system based on deep
reinforcement learning (DRL). Imperfect channel state information (CSI) is
assumed to be available at the base station (BS). To achieve high spectral
efficiency with low time consumption, we propose a novel DRL-based method
called PrecoderNet to design the digital precoder and analog combiner. The DRL
agent takes the digital beamformer and analog combiner of the previous learning
iteration as state, and these matrices of current learning iteration as action.
Simulation results demonstrate that the PrecoderNet performs well in spectral
efficiency, bit error rate (BER), as well as time consumption, and is robust to
the CSI imperfection.
