| This paper considers channel access in a Radio Frequency (RF) powered Internet of Things (IoTs) network that consists of a Successive Interference Cancellation (SIC) capable Hybrid Access Point (HAP) and RF-energy harvesting devices. Unlike prior works, in addition to considering random channel access, both the HAP and these devices have imperfect channel state information. We present two distributed Q-learning based channel access strategies to help sensor devices determine their transmission power. Specifically, these two channel strategies exploit conventional and stateless Q-learning. To investigate the influence of centralized and distributed learning on throughput, we further employ the aforementioned channel strategies in the paradigms of Independent Learner (IL) and Joint Action Learner (JAL). The simulation results show that IL outperforms all other strategies. When there is no channel variation, the IL strategy has up to three times higher throughput than Aloha with SIC. The IL strategy can also achieve 100\% higher throughput than Aloha with SIC when the frame size is one. |