### 统计代写|强化学习作业代写Reinforcement Learning代考|Replay Buffer and Off-Policy Learning

statistics-lab™ 为您的留学生涯保驾护航 在代写强化学习Reinforcement Learning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。我们的专家在代写强化学习Reinforcement Learning代写方面经验极为丰富，各种代写强化学习Reinforcement Learning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

## 统计代写|强化学习作业代写Reinforcement Learning代考|Replay Buffer and Off-Policy Learning

Off-policy learning involves two separate policies: behavior policy $b(a \mid s)$ to explore and generate examples; and $\pi(a \mid s)$, the target policy that the agent is trying to learn as the optimal policy. Accordingly, we could use the samples generated by the behavior policy again and again to train the agent. The approach makes the process sample efficient as a single transition observed by the agent can be used multiple times.
This is called experience replay. The agent is collecting experiences from the environment and replaying those experiences multiple times as part of the learning process. In experience replay, we store the samples (s, a, $r, s^{\prime}$, done) in a buffer. The samples are generated using an exploratory behavior policy while we improve a deterministic target policy using q-values. Therefore, we can always use older samples from a behavior policy and apply them again and again. We keep the buffer size fixed to

some predetermined size and keep deleting the older samples as we collect new ones. The process makes learning sample efficient by reusing a sample multiple time. The rest of the approach remains the same as an off-policy agent.
Let’s apply this approach to the Q-learning agent. This time we will skip giving the pseudocode as there is hardly any change except for using samples from the replay buffer multiple times in each transition. We store a new transition in the buffer and then sample batch_size samples from the buffer. These samples are used to train the Q-agent in the usual way. The agent then takes another step in the environment, and the cycle begins again. Listing4_6.ipynb gives the implementation of the replay buffer and how it is used in the learning algorithm. See Listing 4-6.

## 统计代写|强化学习作业代写Reinforcement Learning代考|Q-Learning for Continuous State Spaces

Until now all the examples we have looked at had discrete state spaces. All the methods studied so far could be categorized as tabular methods. The state action space was represented as a matrix with states along one dimension and actions along the cross-axis.
We will soon transition to continuous state spaces and make heavy use of deep learning to represent the state through a neural net. However, we can still solve many of the continuous state problems with some simple approaches. In preparation for the next chapter, let’s look at the simplest approach of converting continuous values into discrete bins. The approach we will take is to round off continuous floating-point numbers with some precision, e.g., for a continuous state space value between $-1$ to 1 being converted into $-1,-0.9,-0.8, \ldots 0,0.1,0.2, \ldots 1.0$.
listing4_7.ipynb shows this approach in action. We will continue to use the Qlearning agent, experience reply, and learning algorithm from listing4_6. However, this time we will be applying the learning on a continuous environment, that of CartPole, which was described in detail at the beginning of the chapter. The key change that we need is to receive the state values from environment, discretize the values, and then pass this along to the agent as observations. The agent only gets to see the discrete values and uses these discrete values to learn the optimal policy using QAgent. We reproduce in Listing 4-7 the approach used for converting continuous state values into discrete ones. See Figure 4-19.

## 统计代写|强化学习作业代写Reinforcement Learning代考|n-Step Returns

In this section, we will unify the MC and TD approaches. MC methods sample the return from a state until the end of the episode, and they do not bootstrap. Accordingly, MC methods cannot be applied for continuing tasks. TD, on the other hand, uses one-step return to estimate the value of the remaining rewards. TD methods take a short view of the trajectory and bootstrap right after one step.

Both the methods are two extremes, and there are many situations when a middleof-the-road approach could produce lot better results. The idea in $n$-step is to use the rewards from the next $\mathrm{n}$ steps and then bootstrap from $\mathrm{n}+1$ step to estimate the value of the remaining rewards. Figure 4-20 shows the backup diagrams for various values of $n$. On one extreme is one-step, which is the $\mathrm{TD}(0)$ method that we just saw in the context of SARSA, Q-learning, and other related approaches. At the other extreme is the $\infty$-step TD, which is nothing but an MC method. The broad idea is to see that the TD and MC methods are two extremes of the same continuum.

## 统计代写|强化学习作业代写Reinforcement Learning代考|Q-Learning for Continuous State Spaces

Listing4_7.ipynb 展示了这种方法的实际应用。我们将继续使用清单4_6中的Qlearning代理、经验回复和学习算法。然而，这一次我们将把学习应用到一个连续的环境中，即 CartPole 的环境中，这在本章的开头已经详细描述过。我们需要的关键更改是从环境接收状态值，离散化这些值，然后将其作为观察值传递给代理。代理只能看到离散值并使用这些离散值来学习使用 QAgent 的最佳策略。我们在清单 4-7 中重现了用于将连续状态值转换为离散值的方法。请参见图 4-19。

## 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。