Posted on Categories:CS代写, Machine Learning, 机器学习, 计算机代写

# CS代写|机器学习代写Machine Learning代考|COMP5328 Design Details of MARVEL

avatest™

## avatest™帮您通过考试

avatest™的各个学科专家已帮了学生顺利通过达上千场考试。我们保证您快速准时完成各时长和类型的考试，包括in class、take home、online、proctor。写手整理各样的资源来或按照您学校的资料教您，创造模拟试题，提供所有的问题例子，以保证您在真实考试中取得的通过率是85%以上。如果您有即将到来的每周、季考、期中或期末考试，我们都能帮助您！

•最快12小时交付

•200+ 英语母语导师

•70分以下全额退款

## CS代写|机器学习代写Machine Learning代考|DRL Framework

The form of discounted sum is widely adopted in RL because it can cater to both the overall performance of a strategy and the short-term profit. In MARL model, $i$-th agent maintains an individual policy $\pi^i: S \times A^i \rightarrow[0,1]$, and under this policy, an agent can have a state-value function to evaluate the quality of this policy under a state. Since the policy $\pi^i$ is actually an action choice function, the policy quality is usually denoted as $Q_\pi^i\left(s, a^i\right)$ and defined as follows
$$Q_\pi^i\left(s, a^i\right)=\mathbb{E}\pi\left(\sum{k=0}^{\infty} \gamma_k r_{t+k}^i \mid s_t=s, a_t=a\right),$$
where $\mathbb{E}$ denotes the expectation value, and $k$ denotes the action step. This quality function evaluates the action value taken by a policy under a state. However, obtaining future reward from $k=0$ to $k=\infty$ is not applicable for online learning. To solve this problem, the quality function can also be expressed in an iterative fashion:
$$Q^i\left(s_t, a_t^i\right)=\mathbb{E}\left[r_t, \gamma Q^i\left(s_{t+1}, a_{t+1}^i\right)\right]$$

## CS代写|机器学习代写Machine Learning代考|Training Phase

In this section, we discuss how the agents are trained in an MARL model and present the detailed interaction among agents of MARL. The goal of the training process is to teach each controller whether to export or import switches without human interference. The process is shown in Algorithm 2. In the algorithm, Lines 1-8 calculate the utilization of controllers and select the master controller. In lines 2-5, each controller calculates the resource utilization of all controllers in use. Lines 6-7 select the master controller. In lines 9-23, in each iteration, one controller is selected by the master as an actor controller (lines 10-13), and then the actor controller generates a switch migration action and broadcasts the action to other controllers (lines 14-21). Note that the actor is selected according to its resource utilizationbased probability (line 11). As the training continues, each controller is likely to be selected as the actor based on its resource utilization, and thus the actor can be guaranteed with enough training probability.

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

CS代写|机器学习代写Machine Learning代考 请认准UprivateTA™. UprivateTA™为您的留学生涯保驾护航。

## 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。