## 4 Results  
### Simulation Setup and Environment  
The simulations for the proposed 6 DChOA-DRL framework were conducted on a MacBook Air equipped with an Apple M1 chip and 16 GB RAM. The simulation environment was implemented using Python, leveraging the TensorFlow library for constructing and training the DRL framework. TensorFlow provided the computational efficiency and flexibility needed to model the neural networks and dynamically update the agent's policy during training. Additionally, we used the NumPy library for numerical computations and Matplotlib for visualizing results, ensuring seamless processing and representation of the simulation data. For the environment and agent interaction, the OpenAI Gym toolkit was utilized to define the state-action-reward framework and simulate the RIS-aided IoT network. We evaluated the performance of the proposed 6 DChOA-DRL framework using the following simulation setup: all channels were modeled using Rayleigh fading, which typically represents scattering environments, such as those found in smart homes. The path loss exponent was set to 3.5 across all channels. The ambient source signal operated at a frequency of 2.4 GHz with a transmission power of \(P_{s}=20\) dBm, similar to that of a Wi-Fi access point. The source, IoT device, RIS, and IoT receiver were positioned at \([-5,0]\), \([0,0]\), \([0,5]\), and \([5,0]\) meters, respectively, showcasing a batteryless IoT environment enhanced by an indoor RIS [40]. The noise variance at the IoT receiver was set to \(\sigma_{R}^{2}=-70\) dBm. Furthermore, we compared the performance of the proposed 6 DChOA-DRL algorithm with some other benchmark algorithms including RL, a DNN, a convolutional neural network (CNN), the 6 DChOA, the ChOA, and particle swarm optimization (PSO) for the optimization of RIS phase shifts in the studied system model. The experiments were conducted to benchmark the capabilities of these algorithms in enhancing the energy efficiency and data rate performance of IoT devices under realistic conditions. The simulation environment reflects the complexities of IoT networks, where optimizing RIS phase shifts is crucial for both energy harvesting and signal quality. To assess the performance of these algorithms, we employed some key metrics, the RMSE, MAPE, and SD, detailed in Equations (25)-(27). \[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}[x_{i}-\hat{x}_{i}]^{2}} \tag{25}\]  
\[MAPE=\frac{1}{N}\sum_{i=1}^{N}\left|\frac{x_{i}-\hat{x}_{i}}{x_{i}}\right| \times 100 \tag{26}\]  
\[SD=\sqrt{\frac{1}{N}\sum_{i=1}^{N}[x_{i}-\overline{\kappa}]^{2}} \tag{27}\]  
where \(x_{i}\) is the actual value, \(\hat{x}_{i}\) is the predicted value, \(N\) is the total number of data points, and \(\overline{x}\) is the mean of the dataset. The RMSE quantifies the overall error magnitude between the predicted and actual values. A lower RMSE indicates more precise predictions. The MAPE reflects the percentage difference between the predicted and actual values, making it a relative error measure that is particularly useful when comparing across datasets with different scales. The SD is used to assess the consistency of the results across different runs, with a lower SD indicating more stable algorithmic performance. The execution time measures how quickly each algorithm converges to an optimal solution, which is critical for real-time IoT applications where fast decision-making is essential. Finally, convergence curves track the rate at which the algorithms approach the optimal solution, providing insights into their learning dynamics. The use of these evaluation metrics ensures a thorough assessment of the proposed algorithms' capabilities in optimizing RIS phase shifts, highlighting the improvements brought by 6 DChOA-DRL in comparison to traditional methods. Calibrating the hyper-parameters of ML algorithms is crucial for achieving optimal performance, especially in complex tasks like optimizing RIS phase shifts.