In this paper, a Deep Q-Network (DQN) based multi-agent multi-user power allocation algorithm is proposed for hybrid networks composed of radio frequency (RF) and visible light communication (VLC) access points (APs). The users are capable of multihoming, which can bridge RF and VLC links for accommodating their bandwidth requirements. By leveraging a non-cooperative multi-agent DQN algorithm, where each AP is an agent, an online power allocation strategy is developed to optimize the transmit power for providing users' required data rate. Our simulation results demonstrate that DQN's median convergence time training is 90% shorter than the Q-Learning (QL) based algorithm. The DQN-based algorithm converges to the desired user rate in half duration on average while converging with the rate of 96.1% compared to the QL-based algorithm's convergence rate of 72.3% Additionally, thanks to its continuous state-space definition, the DQN-based power allocation algorithm provides average user data rates closer to the target rates than the QL-based algorithm when it converges.

翻译：本文为由无线电频率(RF)和可见光光通信接入点组成的混合网络提出了基于深Q网络(DQN)的多试剂多用户功率分配算法。用户能够进行多光化,可以连接RF和VLC链接以满足其带宽要求。通过利用不合作的多试DQN算法,每个AP都是代理商,制定了在线电力分配战略,优化传输能力,以提供用户所需的数据率。我们的模拟结果表明,DQN的中位趋同时间培训比基于Q-Learing(QL)的算法短90%。基于DQQN的算法平均半时间与理想用户比率趋同,同时与基于QL的算法的72.3%的趋同率为96.1%,此外,基于DQN的电力分配算法由于其持续的国家空间定义,提供的平均用户数据率比基于QL的算法在合并时的目标比率更接近目标率。