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# CS代写|强化学习代写Reinforcement learning代考|CS394R Learning To Control

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## CS代写|强化学习代写Reinforcement learning代考|Learning To Control

A large part of this book considers the problem of learning to predict the distribution of an agent’s returns. In Chapter 7, we will discuss how one might instead learn to maximise or control these returns, and the role that distributional reinforcement learning plays in this endeavour. By learning to control, we classically mean obtaining (from experience) a policy $\pi^$ that maximises the expected return: $$\mathbb{E}{\pi^}\left[\sum{t=0}^{\infty} \gamma^t R_t\right] \geq \mathbb{E}\pi\left[\sum{t=0}^{\infty} \gamma^t R_t\right] \text {, for all } \pi .$$
Such a policy is called an optimal policy. From Section 2.5, recall that the state-action value function $Q^\pi$ is given by
$$Q^\pi(x, a)=\mathbb{E}\pi\left[\sum{t=0}^{\infty} \gamma^t R_t \mid X_0=x, A_0=a\right] .$$
Any optimal policy $\pi^$ has the property that its state-action value function also satisfies the Bellman optimality equation: $$Q^{\pi^}(x, a)=\mathbb{E}\pi\left[R+\gamma \max {a^{\prime} \in \mathcal{A}} Q^{\pi^*}\left(X^{\prime}, a^{\prime}\right) \mid X=x, A=a\right] .$$
Similar in spirit to temporal-difference learning, $Q$-learning is an incremental algorithm that finds an optimal policy. Q-learning maintains a state-action value function estimate, $Q$, which it updates according to
$$Q(x, a) \leftarrow(1-\alpha) Q(x, a)+\alpha\left(r+\gamma \max _{a^{\prime} \in \mathcal{A}} Q\left(x^{\prime}, a^{\prime}\right)\right)$$

## CS代写|强化学习代写Reinforcement learning代考|Further Considerations

Categorical temporal-difference learning learns to predict return distributions from sample experience. As we will see in subsequent chapters, the choices that we made in designing CTD are not unique, and the algorithm is best thought of as a jumping-off point into a broad space of methods. For example, an important question in distributional reinforcement learning asks how we should represent probability distributions, given a finite memory budget. One issue with the categorical representation is that it relies on a fixed grid of locations to cover the range $\left[\theta_1, \theta_m\right]$, which lacks flexibility and is in many situations inefficient. We will take a closer look at this issue in Chapter 5 . In many practical situations we also need to deal with a few additional considerations, including the use of function approximation to deal with very large state spaces (Chapters 9 and 10).

## CS代写|强化学习代写强化学习代考|学习控制

$$Q^\pi(x, a)=\mathbb{E}\pi\left[\sum{t=0}^{\infty} \gamma^t R_t \mid X_0=x, A_0=a\right] .$$

$$Q(x, a) \leftarrow(1-\alpha) Q(x, a)+\alpha\left(r+\gamma \max _{a^{\prime} \in \mathcal{A}} Q\left(x^{\prime}, a^{\prime}\right)\right)$$ 更新该值

## CS代写|强化学习代写Reinforcement learning代考|进一步考虑

. CS代写|强化学习代写

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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。