Abstract: This research explores the use of Q-Learning to enhance energy efficiency and data transmission in RF-powered Internet of Things (IoT) networks. We present a novel Q-Learning strategy integrated with a Hybrid Access Point to significantly improve error correction, polling rounds, network capacity, and message delivery speeds. The study reveals Q-Learning's effectiveness in reducing errors and boosting network performance, outperforming traditional methods like Aloha with Successive Interference Cancellation (Aloha-SIC) and Time Division Multiple Access with Successive Interference Cancellation (TDMA-SIC). Utilizing the Independent Learner paradigm within a distributed Q-Learning framework, we enable sensor devices to dynamically adjust their transmission power based on network conditions, enhancing network efficiency and device energy management. Our findings highlight Q-Learning's success in overcoming the challenges of existing network protocols, enhancing the reliability and performance of RF-powered IoT networks. Additionally, the research illustrates the practical advantages of integrating Q-Learning into IoT systems, including consistent network performance under various conditions and the potential for energy savings. We conclude with a call for the wider adoption of intelligent learning systems in IoT networks to address the demands of connectivity and sustainability, emphasizing Q-Learning's role in advancing IoT connectivity and energy management for the future.

Keywords: Q-Learning, Independent learner paradigm, RF energy harvesting ToT devices, Polling round, Network capacity, Message delivery, Timelines.

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