  This paper studies the joint beamwidth and transmit power optimization
problem in millimeter wave communication systems. A deep reinforcement learning
based approach is proposed. Specifically, a customized deep Q network is
trained offline, which is able to make real-time decisions when deployed
online. Simulation results show that the proposed approach significantly
outperforms conventional approaches in terms of both performance and
complexity. Besides, strong generalization ability to different system
parameters is also demonstrated, which further enhances the practicality of the
proposed approach.
