## 4 Performance Evaluation
Experimental Results and Discussion: In this section, we present the experimental results and discuss our findings in detail. We begin by presenting the results of training the LSTM neural network, providing insights into its performance and learning progress during the training phase. Our evaluation encompasses critical metrics such as network convergence and loss, which allow us to assess its effectiveness in optimizing streaming quality. Next, we conduct a comparative analysis of our proposed model with the state-of-the-art approaches. These comparative experiments take place in a simulated environment with a diverse range of video sequences and different network traces. Our goal is to evaluate the robustness and generalization capabilities of our approach. We meticulously examine key performance indicators, such as video quality, buffer occupancy, rebuffering events, and user experience, providing a comprehensive evaluation of the effectiveness of our approach. For a comprehensive evaluation, we first trained the neural agent network using 70% of the video sequences from the SJTU HDR video sequences dataset. Subsequently, we used the remaining 30% of the dataset videos for the testing process. This deliberate split allows us to effectively appraise the model's performance on unseen data, providing insights into its generalization capabilities beyond the training set. The results presented here represent the average outcomes of all conducted experiments. This approach ensures both reliability and representative assessment of our approach's performance. Our intention in presenting these results is to demonstrate the effectiveness and potential advantages inherent in our proposed approach in optimizing video streaming quality and elevating user experience. Figure 6 illustrates the degree of performance improvement achieved when using 70% of the video sequences from the dataset and how it converged with the amount of data. The graph showcases the progress of performance improvement as the training process advances, with the \(x\)-axis representing the percentage of data used for training. As depicted in the graph, performance improvement consistently increases as more of the dataset is used for training. However, it becomes evident that the rate of improvement starts to converge most clearly by about 60% of the dataset being used for training. This graph provides insights into the effectiveness of our approach in improving performance. Moreover, it helps identify the saturation point, where additional training data may not significantly improve performance. This means that the agent is well trained and ready for deployment (streaming). Figure 7 shows the degree of performance improvement achieved over time and when it converges. The graph showcases the evolution of performance improvement as the training process advances, with the \(x\)-axis representing the time in hours. This experiment was conducted using 70% of video sequences and Network Dataset Norway in real time. As shown in Figure 7, performance improvement consistently increases throughout the experiment and starts to converge after about 40 h. Therefore, we stopped the experiment in 60 h when the desired amount of data was achieved. Additionally, more training time may not significantly improve performance. Figure 8 shows valuable insights into the effectiveness of utilizing a secondary agent and its impact of performance improvement. It demonstrates the importance of collaborative approaches in enhancing the overall system's decision-making capabilities and optimizing performance. In this experimental scenario, we conducted training for the main neural network agent while introducing a secondary agent into the training process to evaluate its impact on performance improvement. Throughout the duration of the experiment, the overall system continuously monitored the demand for intervention and promptly dispatched alert messages to the secondary agent in cases involving penalties or erroneous decisions.