### Related Works, Research Gaps, and Motivations
By strategically placing RISs in the environment, the wireless signals can be redirected or enhanced to improve the signal strength, extending the transmission range of batteryless IoT devices without increasing their energy consumption [30; 31]. Moreover, RISs can improve spectral efficiency by optimizing the reflection of signals, minimizing interference, and maximizing the usable bandwidth. This not only enhances communication reliability but also increases the energy harvesting capability of IoT devices, as stronger and more directed signals improve the efficiency of energy capture from ambient sources. Through these capabilities, RISs play a crucial role in addressing the communication and energy limitations faced by batteryless IoT networks, making them a key technology for future IoT deployments [32]. Optimizing RIS elements to maximize both the energy harvesting capability in IoT devices and the achieved data rate at the IoT receiver presents several challenges that have been overlooked in the literature. The complexity arises from the need to carefully adjust the phase shifts of the RIS elements to simultaneously enhance the signal strength for energy harvesting and improve communication efficiency without introducing interference. Achieving this balance is critical, as any misalignment in the phase optimization could lead to suboptimal energy harvesting or degraded signal quality at the receiver. In such a scenario, an RIS can be deployed either in the forward link, which refers to the ambient source-to-IoT device link, or in the IoT device-to-IoT receiver link. When placed in the forward link, the RIS reflects ambient signals toward the IoT device, enhancing the harvested energy, improving achievable data rates, and extending the communication range without requiring any modifications or additional processing at the IoT device itself. This paper investigates the scenario where the RIS is positioned to increase the IoT device activation distance and the received power by delivering more energy to the IoT device. However, a significant challenge arises because the reflected signal from the RIS must not overpower the IoT device's signal. If the reflected signal is too strong, it can interfere with the IoT device's transmission, resulting in a degraded decoding performance at the IoT receiver. To avoid this, the interference caused by the reflected signal needs to be carefully controlled and minimized. Thus, our goal is to enhance the IoT device's data rate by maximizing the signal-to-interference-plus-noise ratio (SINR) at the IoT receiver. By doing so, we aim to improve the desired signal power received by the IoT receiver while simultaneously suppressing interference from both the direct source-to-receiver link and the indirect source-to-RIS-to-receiver link. To achieve this, we optimize the phase shifts of the RIS elements to maximize the IoT device's data rate, subject to the IoT device's energy harvesting constraint. However, the optimization problem is complex and non-convex, making it difficult to solve using traditional methods. To overcome this, we propose a novel optimization approach based on deep reinforcement learning (DRL) and a six-dimensional chimp optimization algorithm (6D-ChOA). This hybrid optimization framework allows us to effectively solve the phase-shift optimization problem, enabling us to achieve the dual objectives of maximizing both the IoT device's energy harvesting capability and its data rate, as demonstrated in this paper.