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### 您將學到的內容有

Build a Reinforcement Learning system for sequential decision making.

Understand the space of RL algorithms (Temporal- Difference learning, Monte Carlo, Sarsa, Q-learning, Policy Gradients, Dyna, and more).

Understand how to formalize your task as a Reinforcement Learning problem, and how to begin implementing a solution.

Understand how RL fits under the broader umbrella of machine learning, and how it complements deep learning, supervised and unsupervised learning

### 您將獲得的技能

## 關於此 專項課程

## 應用的學習項目

Through programming assignments and quizzes, students will:

Build a Reinforcement Learning system that knows how to make automated decisions.

Understand how RL relates to and fits under the broader umbrella of machine learning, deep learning, supervised and unsupervised learning.

Understand the space of RL algorithms (Temporal- Difference learning, Monte Carlo, Sarsa, Q-learning, Policy Gradient, Dyna, and more).

Understand how to formalize your task as a RL problem, and how to begin implementing a solution.

#### 可分享的證書

#### 100% 在線課程

#### 靈活的計劃

#### 中級

Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode

#### 完成時間大約為3 個月

#### 英語（English）

### 專項課程的運作方式

### 加入課程

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### 實踐項目

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### 獲得證書

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### 此專項課程包含 4 門課程

### Fundamentals of Reinforcement Learning

Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making.

### Sample-based Learning Methods

In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning.

### Prediction and Control with Function Approximation

In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem---function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment.

### A Complete Reinforcement Learning System (Capstone)

In this final course, you will put together your knowledge from Courses 1, 2 and 3 to implement a complete RL solution to a problem. This capstone will let you see how each component---problem formulation, algorithm selection, parameter selection and representation design---fits together into a complete solution, and how to make appropriate choices when deploying RL in the real world. This project will require you to implement both the environment to stimulate your problem, and a control agent with Neural Network function approximation. In addition, you will conduct a scientific study of your learning system to develop your ability to assess the robustness of RL agents. To use RL in the real world, it is critical to (a) appropriately formalize the problem as an MDP, (b) select appropriate algorithms, (c ) identify what choices in your implementation will have large impacts on performance and (d) validate the expected behaviour of your algorithms. This capstone is valuable for anyone who is planning on using RL to solve real problems.

### 提供方

#### 阿尔伯塔大学

UAlberta is considered among the world’s leading public research- and teaching-intensive universities. As one of Canada’s top universities, we’re known for excellence across the humanities, sciences, creative arts, business, engineering and health sciences.

#### Alberta Machine Intelligence Institute

The Alberta Machine Intelligence Institute (Amii) is home to some of the world’s top talent in machine intelligence. We’re an Alberta-based

### 審閱

#### 4.7

##### 來自强化学习的熱門評論

Great course! Lots of hands-on RL algorithms. I'm looking forward to the next course in the specialization.

Excellent final course for the specialization. Moon Lander project was informative and fun.

Well peaced and thoughtfully explained course. Highly recommended for anyone willing to set solid grounding in Reinforcement Learning. Thank you Coursera and Univ. of Alberta for the masterclass.

I understood all the necessary concepts of RL. I've been working on RL for some time now, but thanks to this course, now I have more basic knowledge about RL and can't wait to watch other courses

Concepts are bit hard, but it is nice if you undersand it well, espically the bellman and dynamic programming.\n\nSometimes, visualizing the problem is hard, so need to thoroghly get prepared.

The comments given by the auto grader is not informative of the errors causing problem, and not sensitive enough to capture problems with action selection steps based on current state.

An excellent introduction to the subject of Reinforcement Learning, accompanied by a very clear text book. The python assignments in Jupyter notebooks are both informative and helpful.

Really great resource to follow along the RL Book. IMP Suggestion: Do not skip the reading assignments, they are really helpful and following the videos and assignments becomes easy.

## 常見問題

退款政策是如何规定的？

我可以只注册一门课程吗？

可以！点击您感兴趣的课程卡开始注册即可。注册并完成课程后，您可以获得可共享的证书，或者您也可以旁听该课程免费查看课程资料。如果您订阅的课程是某专项课程的一部分，系统会自动为您订阅完整的专项课程。访问您的学生面板，跟踪您的进度。

有助学金吗？

我可以免费学习课程吗？

此课程是 100% 在线学习吗？是否需要现场参加课程？

此课程完全在线学习，无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。

完成专项课程需要多长时间？

It is recommended that learners take between 4-6 months to complete the specialization.

What background knowledge is necessary?

Recommended that learners have at least one year of undergraduate computer science or 2-3 years of professional experience in software development. Experience and comfort with programming in Python required. Must be comfortable converting algorithms and pseudocode into Python. Basic understanding of concepts from statistics (distributions, sampling, expected values), linear algebra (vectors and matrices), and calculus (computing derivatives)

Do I need to take the courses in a specific order?

Yes, it is recommended that courses are taken sequentially.

完成专项课程后我会获得大学学分吗？

Learners that complete the specialization will earn a Coursera specialization certificate signed by the professors of record, not a University of Alberta credit.

What will I be able to do upon completing the Specialization?

By the end of this specialization, you will be able to"

- Build a Reinforcement Learning system for sequential decision making.
- Understand the space of RL algorithms (Temporal- Difference learning, Monte Carlo, Sarsa, Q-learning, Policy Gradients, Dyna, and more).
- Understand how to formalize your task as a Reinforcement Learning problem, and how to begin implementing a solution.
- Understand how RL fits under the broader umbrella of machine learning, and how it complements deep learning, supervised and unsupervised learning

還有其他問題嗎？請訪問 學生幫助中心。