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CS代写|机器学习代写Machine Learning代考|COMP7703 Simulation Results

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CS代写|机器学习代写Machine Learning代考|Average End-to-End Delay

Figure $2.5$ shows the average end-to-end transmission delay of different schemes under different test scenarios. In OMNet++ 4.6, each packet can be labeled with a start time at the source node, and its completion time can be acquired at the destination node. Therefore, we can calculate the end-to-end delay of a flow using the completion time and the start time of packets in the flow. The total traffic intensity of each flow in the figure increase from 10 to $25 \mathrm{Mbps}$, the $R P$ of the traffic is set to $0.1$, and critical link ratio $\alpha$ is set to $0.3$. In the figure, as the scale of the topology rises, the average end-to-end transmission delay increases.

Among all the schemes, ScaleDRL presents the lowest delay because its congestion situation is better than others. In Fig. 2.5a, ScaleDRL’s average delay reduces by up to 39 and $54 \%$, compared with DRL-TE and TIDE, respectively. TIDE experiences severe performance degradation as the network scale increases due to the curse of dimensionality problem. Actually, DRL-TE also experiences the curse of dimensionality problem in all the scenarios since it needs to handle all flows, whose required action space is too large.

CS代写|机器学习代写Machine Learning代考|Training Process

We take the training process of TIDE, DRL-TE, and ScaleDRL under the large topology with 55 nodes as an example to illustrate impact of the curse of dimensionality problem on DRL-based TE schemes. Figure $2.6$ shows the training process of three algorithms, and each episode contains one thousand training steps. Due to the problem of curse of dimensionality, the training reward of TIDE and DRLTE maintains at a low level in spite of the increase of the training episodes. In contrast, the training reward of ScaleDRL increases significantly at the first part of the training and stays relatively stable when the model converges.

CS代写|机器学习代写Machine Learning代考|Training Process

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MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。