In this letter, we investigate the hybrid beamforming based on deep reinforcement learning (DRL) for millimeter Wave (mmWave) multi-user (MU) multiple-input-single-output (MISO) system. A multi-agent DRL method is proposed to solve the exploration efficiency problem in DRL. In the proposed method, prioritized replay buffer and more informative reward are applied to accelerate the convergence. Simulation results show that the proposed architecture achieves higher spectral efficiency and less time consumption than the benchmarks, thus is more suitable for practical applications.

翻译：在此信中,我们调查基于对毫米波(mmWave)多用户(MU)多投入单产出(MISO)系统的深度强化学习(DRL)的混合波束成型(DRL),建议采用多剂DRL方法解决DRL的勘探效率问题。在拟议方法中,优先重播缓冲和给予更多资料的奖励以加速趋同。模拟结果表明,拟议的结构实现的光谱效率更高,比基准的消耗时间更少,因此更适合实际应用。