经济代写|博弈论代写Game Theory代考|ECON40010

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|Security Problems of Reinforcement Learning

Understanding adversarial attacks on RL systems is essential to develop effective defense mechanisms and an important step toward trustworthy and safe RL. The reliable implementation of RL techniques usually requires accurate and consistent feedback from the environment, precisely and timely deployed controls to the environment and reliable agents (in multi-agent RL cases). Lacking any one of the three factors will render failure to learn optimal decisions. These factors can be used by adversaries as gateways to penetrate RL systems. It is hence of paramount importance to understand and predict general adversarial attacks on RL systems targeted at the three doorways. In these attacks, the miscreants know that they are targeting RL systems, and therefore, they tailor their attack strategy to mislead the learning agent. Hence, it is natural to start with understanding the parts of RL that adversaries can target at.

Figure 19.1 illustrates different types of attacks on the RL system. One type of attack aims at the state which is referred to as state attacks. Attacks on state signals can happen if the remote sensors in the environment are compromised or the communication channel between the agent and the sensors is jammed or corrupted. In such circumstances, the learning agent may receive a false state observation $\tilde{\zeta}_k$ of the actual state $i_k$ at time $k$ and/or may receive a delayed observation of the actual state or even never receive any information regarding the state at time $k$. An example of effortless state attacks is sequential blinding/blurring of the cameras in a deep RL-based autonomous vehicle via lasers/dirts on lens, which can lead to learning false policies and hence lead to catastrophic consequences. Based on its impact on the RL systems, state attacks can be classified into two groups: (i) denial of service (DoS) and (ii) integrity attacks. The main purpose of DoS attacks is to deny access to sensor information. Integrity attacks are characterized by the modification of sensor information, compromising their integrity.

Another type of attacks targets at cost signals. In this type of attacks, the adversary aims to corrupt the cost signals that the learning agent has received with a malicious purpose of misleading the agent. Instead of receiving the actual cost signal $g_k=g\left(i_k, u_k, j_{k+1}\right)$ at time $k$, the learning agent receives a manipulated or falsified cost signal $g_k$. The corruption of cost signals comes from the false observation of the state in cases where the cost is predetermined by the learning agent. In other cases where cost signals are provided directly by the environment or a remote supervisor, the cost signal can be corrupted independently from the observation of the state. The learning agent receives falsified cost signals even when the observation of the state is accurate. In the example of autonomous vehicle, if the cost depends on the distance of the deep RL agent to a destination as measure by GPS coordinates, spoofing of GPS signals by the adversary may result in incorrect reward signals, which can translate to incorrect navigation policies (see Behzadan and Munir, 2018).

经济代写|博弈论代写Game Theory代考|Reinforcement Learning with Manipulated Cost Signals

Under malicious attacks on cost signals as we have discussed in Section 19.2, the RL agent will fail to observe the actual cost feedback from the environment. Instead, the agent receives a cost signal that might be falsified by the adversary. Consider the following MDP with falsified cost denoted by $\langle S, A, g, \tilde{g}, P, \alpha\rangle$. Under the falsification, the agent, instead of receiving the actual cost signal $g_t \in \mathbb{R}$ at the $t$ th update, observes a falsified cost signal denoted by $\tilde{g}_t \in \mathbb{R}$. The remaining aspects of the MDP framework stay the same. The adversary’s task here is to design falsified cost signals $\tilde{g}$ based on his information structure and the actions available to him so that he can achieve certain objectives. Suppose the adversary is an insider and he knows what the agent knows at time $t$. In other words, at time $t$, the adversary knows the state trajectory, the control trajectory, and the cost signals up to time $t$. The adversary may or may not know the system model. Suppose that the adversary falsifies the cost signals in a stealthy way. In this case, the cost associated with each state-control-state triple is consistently falsified in the same way. The following definition gives a formal definition of the attack.

Definition 19.1 (Stealthy Attacks) If $\tilde{g}t$ takes the same value for the same state-control-state triple for all $t$; i.e. for $t \neq \tau$, we have $\tilde{g}_t=\tilde{g}\tau$ if $\left(i_t, u_t, j_{t+1}\right)=\left(i_t, u_\tau, j_{\tau+1}\right)$, then we say the attacks on the cost signals are stealthy.

Under stealthy attacks, the falsified cost signals can be given by a function $\tilde{g}: S \times A \times S \rightarrow \mathbb{R}$. Then, at time $t$, the falsified cost the agent receives is $\tilde{g}t=\tilde{g}\left(i_t, u_t, i{t+1}\right)$. Since the transition from $i_t$ to $i_{t+1}$ depends on the transition probability, without loss of generality, we consider only the cost structure defined on the state-control pair, i.e. $g(i, u)$ for $i \in S, u \in A$.

博弈论代考

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 :)