## 计算机代写|强化学习代写Reinforcement learning代考|COMP579

2022年12月24日

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## 计算机代写|强化学习代写Reinforcement learning代考|OpenAI Gym

OpenAI has created the Gym suite of environments for Python, which has become the de facto standard in the field [11]. The Gym suite can be found at OpenAI ${ }^4$ and on GitHub. ${ }^5$ Gym works on Linux, macOS, and Windows. An active community

exists, and new environments are created continuously and uploaded to the Gym website. Many interesting environments are available for experimentation, to create your own agent algorithm for, and test it.

If you browse Gym on GitHub, you will see different sets of environments, from easy to advanced. There are the classics, such as Cartpole and Mountain car. There are also small text environments. Taxi is there, and the Arcade Learning Environment [6], which was used in the paper that introduced DQN [36], as we will discuss at length in the next chapter. MuJoCo ${ }^6$ is also available, an environment for experimentation with simulated robotics [54], or you can use PyBullet. ${ }^7$

You should now install Gym. Go to the Gym page on https://gym.openai.com and read the documentation. Make sure Python is installed on your system (does typing Python at the command prompt work?) and that your Python version is up to date (version $3.10$ at the time of this writing). Then type

to install Gym with the Python package manager. Soon, you will also be needing deep learning suites, such as TensorFlow or PyTorch. It is recommended to install Gym in the same virtual environment as your upcoming PyTorch and TensorFlow installation, so that you can use both at the same time (see Sect. B.3.3.1). You may have to install or update other packages, such as numpy, scipy, and pyglet, to get Gym to work, depending on your system installation.

You can check if the installation works by trying if the CartPole environment works, see Listing 2.2. A window should appear on your screen in which a Cartpole is making random movements (your window system should support OpenGL, and you may need a version of pyglet newer than version $1.5 .11$ on some operating systems).

## 计算机代写|强化学习代写Reinforcement learning代考|Taxi Example with Value Iteration

The Taxi example (Fig. 2.8) is an environment where taxis move up, down, left, and right, and pick up and drop off passengers. Let us see how we can use value iteration to solve the Taxi problem. The Gym documentation describes the Taxi world as follows. There are four designated locations in the Grid world indicated by R(ed), B(lue), G(reen), and Y(ellow). When the episode starts, the taxi starts off at a random square and the passenger is at a random location. The taxi drives to the passenger’s location, picks up the passenger, drives to the passenger’s destination (another one of the four specified locations), and then drops off the passenger. Once the passenger is dropped off, the episode ends.

The Taxi problem has 500 discrete states: there are 25 taxi positions, five possible locations of the passenger (including the case when the passenger is in the taxi), and 4 destination locations $(25 \times 5 \times 4)$.

The environment returns a new result tuple at each step. There are six discrete deterministic actions for the Taxi driver:
0 : Move south
1: Move north
2: Move east
3: Move west
4: Pick up passenger
5: Drop off passenger
There is a reward of $-1$ for each action and an additional reward of $+20$ for delivering the passenger, and a reward of $-10$ for executing actions pickup and dropoff illegally.

The Taxi environment has a simple transition function, which is used by the agent in the value iteration code. ${ }^8$ Listing $2.3$ shows an implementation of value iteration that uses the Taxi environment to find a solution. This code is written by Mikhail Trofimov and illustrates clearly how value iteration first creates the value function for the states, and then that a policy is formed by finding the best action in each state, in the build-greedy-policy function. ${ }^9$

To get a feeling for how the algorithms work, please use the value iteration code with the Gym Taxi environment, see to Listing 2.3. Run the code, and play around with some of the hyperparameters to familiarize yourself a bit with Gym and with planning by value iteration. Try to visualize for yourself what the algorithm is doing. This will prepare you for the more complex algorithms that we will look into next.

# 强化学习代考

## 计算机代写|强化学习代写Reinforcement learning代考|OpenAI Gym

OpenAI 为 Python 创建了 Gym 环境套件，这已成为该领域的事实标准 [11]。健身房套件可以在 OpenAI 找到4在 GitHub 上。5Gym 适用于 Linux、macOS 和 Windows。一个活跃的社区

## 计算机代写|强化学习代写Reinforcement learning代考|Taxi Example with Value Iteration

0：向南移动
1：向北移动
2：向东移动
3：向西移动
4：上客
5：下客

Taxi 环境有一个简单的转换函数，由代理在值迭代代码中使用。8清单2.3展示了使用 Taxi 环境寻找解决方案的价值迭代的实现。这段代码由 Mikhail Trofimov 编写，清楚地说明了价值迭代如何首先为状态创建价值函数，然后通过在 build-greedy-policy 函数中找到每个状态中的最佳动作来形成策略。9

## 有限元方法代写

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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。