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# 计算机代写|基础编程代写Fundamental of Programming代考|MIT503 Graphs

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## 计算机代写|基础编程代写Fundamental of Programming代考|Graphs

Section 1.2.1 defined a learner as the smallest ‘module’ whereas Sect. 1.2.2 provided a mechanism for organizing learners into teams without prior parameterization for how many learners should appear in a team. ${ }^2$ Different teams might excel at defining policy for different subsets of the state-action sequence. Typically, it is assumed that crossover will provide a sufficient mechanism for recombining the properties from different teams. The underlying premise to this is that the learners when merged using crossover continue to identify unique conditions under which to out-bid other learners. Unfortunately, there is no guarantee that this will be the case. TPG may avoid this condition by enabling a learner to instead reference a different team, thus devolving control to the referenced team under state $\mathbf{s}_t$.

The key to this process is to provide two types of learner action mutation. At initialization all learners are initialized from a discrete set of atomic actions, $a(i) \in A$, specific to the task (e.g. an enumeration of all joystick directions). Thereafter, an action mutation consists of the sequence of tests summarized by Algorithm 1.1. Step 1 determines whether to apply any form of mutation. When true either an action from the set of atomic actions, $A$, is chosen (Step 5) or a pointer to another team, $T$, is established (Step 6). The significance of Step 4 is that it potentially forces a change in the type of action.

## 计算机代写|基础编程代写Fundamental of Programming代考|Memory

The partially observable aspects of the ViZDoom task imply that support for memory is beneficial [22], even with respect to single ViZDoom source tasks [15]. For the purposes of this study, we will adopt the probabilistic indexed memory formulation previously benchmarked under ViZDoom and Dota 2 reinforcement learning environments $[15,21,22]$. In summary, only one instance of indexed memory is retained. This implies that a TPG agent inherits the state of indexed memory left by the previous agent. Indexed memory therefore represents a global internal model of state that is never reset. Registers, $R$, specific to a learner (Sect. 1.2.1) are considered to capture the internal state of each learner. With this in mind, the instruction set is augmented with a write (write (R)) and read $(\mathrm{R}[\mathrm{i}]=\operatorname{read}(\mathrm{k}))$ operation. Write operations are probabilistic, distributing the content of a learner’s registers across $L$ columns of indexed memory. The probability of performing a write is such that locations towards column 1 and $L$ are less likely to be written to (or long term memory). Conversely, locations near $\frac{L}{2}$ are most likely to be written to (or short term memory). Read operations specify a target register, $R[i]$, and an ‘address’ $(k)$ to indexed memory, i.e. $0<k \leq L \times$ MaxReg. Further details of the probabilistic indexed memory model can be found in earlier work $[15,21,22]$.

## 计算机代写|基础编程代写Fundamental of Programming代考|Memory

ViZDoom 任务的部分可观察方面意味着对内存的支持是有益的 [22]，即使对于单个 ViZDoom 源任务 [15] 也是如此。出于本研 究的目的，我们将采用先前在 ViZDoom 和 Dota 2 强化学习环境下进行基准测试的概率索引记忆公式[15, 21, 22]. 总之，只保留 了一个索引内存实例。这意味着 TPG 代理继承了前一个代理留下的索引内存状态。因此，索引内存代表了一个永远不会重置的全 局内部状态模型。泀存器， $R$ ，特定于学习者（第 $1.2 .1$ 节）被认为是捕捉每个学习者的内部状态。考虑到这一点，指令集增加了 写 (写 (R) ) 和读 $(\mathrm{R}[\mathrm{i}]=\operatorname{read}(\mathrm{k}))$ 手术。写操作是概率性的，将学习青奇存器的内容分布到 $L$ 索引内存的列。执行写入的概率 是这样的位置朝向第 1 列和 $L$ 不太可能被写入（或长期记忆）。反之，靠近的位置 $L$ 最有可能被写入（或短期记忆）。读取操作指 定一个目标拿存器， $R[i]$ 和一个”地址” $(k)$ 索引内存，即 $0<k \leq L \times$ 最大注册。概率索引记忆模型的更多细节可以在早期的工作 中找到 $[15,21,22]$

## 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。