  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.
