###### Abstract  
The rapid advancement of Internet of Things (IoT) networks has revolutionized modern connectivity by integrating many low-power devices into various applications. As IoT networks expand, the demand for energy-efficient, batteryless devices becomes increasingly critical for sustainable future networks. These devices play a pivotal role in next-generation IoT applications by reducing the dependence on conventional batteries and enabling continuous operation through energy harvesting capabilities. However, several challenges hinder the widespread adoption of batteryless IoT devices, including the limited transmission range, constrained energy resources, and low spectral efficiency in IoT receivers. To address these limitations, reconfigurable intelligent surfaces (RISs) offer a promising solution by dynamically manipulating the wireless propagation environment to enhance signal strength and improve energy harvesting capabilities. In this paper, we propose a novel deep reinforcement learning (DRL) algorithm that optimizes the phase shifts of RISs to maximize the network's achievable rate while satisfying IoT devices' energy harvesting constraints. Our DRL framework leverages a novel six-dimensional chimp optimization algorithm (6 DchoA) to fine-tune the hyper-parameters, ensuring efficient and adaptive learning. The proposed 6 DchoA-DRL algorithm optimizes RIS phase shifts to enhance the received power of IoT devices while mitigating interference from direct and RIS-cascaded links. The simulation results demonstrate that our optimized RIS design significantly improves energy harvesting and achievable data rates under various system configurations. Compared to benchmark algorithms, our approach achieves higher gains in harvested power, an improvement in the data rate at a transmit power of 20 dBm, and a significantly lower root mean square error (RMSE) of 0.13 compared to 3.34 for standard RL and 6.91 for the DNN, indicating more precise optimization of RIS phase shifts. Internet of Things; energy harvesting; reconfigurable intelligent surfaces; advanced deep reinforcement learning; chimp optimization algorithm [table]capposition=top =,leftmargin=10 pt,rightmargin=10 pt,top =,bottommargin=10 pt,rightmargin=10 pt,top =,bottommargin=10 pt,rightmargin=10 pt,top =,bottommargin=10 pt,rightmargin=10 pt,top =,bottommargin=10 pt,rightmargin=10 pt,top =,bottommargin=10 pt,rightmargin=1  
smart environments [5; 6]. As the IoT continues to expand, ensuring its devices' energy efficiency and sustainability is essential for supporting the future of this interconnected ecosystem [7]; therefore, the demand for low-power, batteryless IoT devices has grown significantly. These devices are critical for the sustainability and longevity of IoT ecosystems, as they help reduce the dependence on traditional batteries, which pose both operational and environmental challenges. Battery-powered IoT devices require regular maintenance, replacements, and disposal, contributing to electronic waste and increasing operational costs [8]. In contrast, batteryless IoT devices can harness ambient energy through advanced energy harvesting techniques, enabling continuous operation with minimal maintenance [9; 10]. This shift towards low-power, self-sustaining IoT devices is essential for building scalable, cost-effective, and environmentally friendly IoT infrastructures, particularly in remote or hard-to-reach environments where frequent battery replacement is impractical. A promising technology for batteryless, energy harvesting IoT systems is backscatter communication, as it enables devices to transmit data by reflecting existing signals rather than generating their own [11; 12; 13; 14]. This drastically reduces power consumption, allowing IoT devices to operate using minimal energy harvested from ambient sources, such as solar or radiofrequency (RF) signals [15].