# 经济代写|博弈论代写Game Theory代考|ECON3503

#### Doug I. Jones

Lorem ipsum dolor sit amet, cons the all tetur adiscing elit

couryes-lab™ 为您的留学生涯保驾护航 在代写博弈论Game Theory方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写博弈论Game Theory代写方面经验极为丰富，各种代写博弈论Game Theory相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础
couryes™为您提供可以保分的包课服务

## 经济代写|博弈论代写Game Theory代考|Case Study

Here, we consider TD learning on random walk. Given a policy $\mu$, an MDP can be considered as a Markov cost process, or MCP. In this MCP, we have $n=20$ states. The transition digram of the $\mathrm{MCP}$ is given in Figure 19.2. At state $i=i_k, k=2,3, \ldots, n-1$, the process proceed either left to $i_{k-1}$ or right to $i_{k+1}$, with equal probability. The transition at states from $i_2$ to $i_{n-1}$ is similar to symmetric one-dimensional random walk. At state $i_1$, the process proceed to state $i_2$ with probability $1 / 2$ or stays at the same state with equal probability. At state $i_n$, the probabilities of transition to $i_{n-1}$ and staying at $i_n$ are both $\frac{1}{2}$. That is, we have $p_{i_k i_{k+1}}\left(\mu\left(i_k\right)\right)=p_{i_k i_{k-1}}\left(\mu\left(i_k\right)\right)=\frac{1}{2}$ for $k=2,3, \ldots, n-1, p_{i_1 i_1}=$ $p_{i_1 l_2}=\frac{1}{2}$ and $p_{i_n l_n}=p_{i_n i_{n-1}}=\frac{1}{2}$. The cost at state $i_k$ is set to be $k$ if $k \leq 10$ and $21-k$ if $k>10$. That is,
$$g\left(i_k, \mu\left(i_k\right)\right)=\left{\begin{array}{ll} k & \text { if } k \leq 10 \ 21-k & \text { else } \end{array} .\right.$$
We consider the discount factor $\alpha=0.9$. The task here is to use approximate $\operatorname{TD}(\lambda)$ learning algorithm to esitimate and approximate the cost-to-go function $J^\mu$ of this MCP. We consider a linear parametrization of the form
$$J(i, r)=r(3) i^2+r(2) i+r(1)$$
and $r=(r(1), r(2), r(3)) \in \mathbb{R}^3$. Suppose the learning agent updates $r_t$ based on $\operatorname{TD}(\lambda)$ learning algorithm (19.3) and (19.4) and tries to find an estimate of $J^\mu$. We simulate the MCP and obtain a trajectory that long enough and its associated cost signals. We need an infinite long trajectory ideally. But here, we set the length of the trajectory to be $10^5$. We run, respectively, $\operatorname{TD}(1)$ and $\operatorname{TD}(0)$ on the same simulated trajectory based on rules given in (19.3) and (19.4). The black line indicates the cost-to-go function of the MCP. The blue markers are the approximations of the cost-to-go function obtained by following the $\operatorname{TD}(\lambda)$ algorithm (19.3) and (19.4) with $\lambda=1$ and $\lambda=0$. We can see that $J_{\mathrm{TD(1)}}$ and $J_{\mathrm{TD(0)}}$ is a quadratic function of $i$ as we set in (19.21). Both $J_{\mathrm{TD}(1)}$ and $J_{\mathrm{TD(0)}}$ can serve a fairly good approximation of $J^\mu$ as we can see. The dimension of the parameters we need to update goes from $n=20$ in the $\operatorname{TD}(\lambda)$ algorithm (19.2) to $K=3$ in the approximation counterpart (19.3) which is more efficient computationally.

## 经济代写|博弈论代写Game Theory代考|Motivation and Challenges

Military tactical networks often suffer from severe resource constraints (e.g. battery, computing power, bandwidth, and/or storage). This puts a high challenge in designing a network (or system) which should be robust against adversaries under high dynamics in tactical environments. When a network consists of highly heterogeneous entities, such as Internet-of-Things (IoT) devices in a large-scale network, deploying multiple defense mechanisms to protect a system requires high intelligence to meet conflicting system goals of security and performance. When a node fails due to being compromised or functional fault, multiple strategies can be considered to deal with this node failure, such as destruction, repair, or replacement. What strategy to take to deal with this node is vital for the system to complete a given mission as well as to defend against adversaries because nodes themselves are network resources for providing destined services and defense/security. If a node detected as compromised is not useful in a given system, it can be discarded (i.e. disconnected or destroyed) based on the concept of “disposable security” (Kott et al. 2016) or “self-destruction” (Brueckner et al. 2014; Curiac et al. 2009; Zeng et al. 2010). However, if the node is regarded as a highly critical asset which keeps highly confidential information or provides critical services (e.g. web servers or databases), its removal will cause a critical damage to service provision and/or may introduce security breach. To obtain optimal intrusion response strategies to deal with nodes detected as compromised/failed, we propose a bio-inspired multilayer network structure that consists of three layers including the core layer, the middle layer, and the outer layer where the nodes are placed in each layer according to their importance (i.e. place the most important nodes in the core layer, the medium important nodes in the middle layer, and the least important nodes in the outer layer). This network design is bio-inspired by mimicking a defense system of the human body upon pathogen attacks and how the body deals with an infection aiming to reach inner organs (Janeway et al. 1996).

# 博弈论代考

## 经济代写|博弈论代写Game Theory代考|Case Study

$i=i_k, k=2,3, \ldots, n-1$ ，过程继续进行 $i_{k-1}$ 或对 i_ ${\mathrm{k}+1}$ 的权利 $i_{k+1}$ ，概率相等。状态的转换 $i_2$ 到 $i_{n-1}$ 类似 于对称的一维随机游走。在状态 $i_1$ ，过程继续状态 $i_2$ 有概 率 $1 / 2$ 或以相同的概率保持在相同的状态。在状态 $i_n$ ，过 渡到的概率 $i_{n-1}$ 并留在 $i_n$ 两者都是 $\frac{1}{2}$. 也就是说，我们有 $p_{i_k i_{k+1}}\left(\mu\left(i_k\right)\right)=p_{i_k i_{k-1}}\left(\mu\left(i_k\right)\right)=\frac{1}{2}$ 为了 $k=2,3, \ldots, n-1, p_{i_1 i_1}=p_{i_1 l_2}=\frac{1}{2}$ 和 $p_{i_n l_n}=p_{i_n i_{n-1}}=\frac{1}{2}$. 状态 $_\mathrm{k}$ 的成本 $i_k$ 设置为 $\mathrm{k} k$ 如果 $k \leq 10$ 和 $21-k$ 如果 $k>10$. 即 $\$ \
$k \quad$ if $k \leq 1021-k \quad$ else
。正确的。

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

Days
Hours
Minutes
Seconds

# 15% OFF

## On All Tickets

Don’t hesitate and buy tickets today – All tickets are at a special price until 15.08.2021. Hope to see you there :)