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.
提供方
阿尔伯塔大学
Alberta Machine Intelligence Institute
强化学习 专项课程阿尔伯塔大学课程信息
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第 1 门课程（共 4 门）
中级
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
完成时间大约为15 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
您将学到的内容有
Formalize problems as Markov Decision Processes
Understand basic exploration methods and the exploration / exploitation tradeoff
Understand value functions, as a general-purpose tool for optimal decision-making
Know how to implement dynamic programming as an efficient solution approach to an industrial control problem
您将获得的技能
- Artificial Intelligence (AI)
- Machine Learning
- Reinforcement Learning
- Function Approximation
- Intelligent Systems
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 1 门课程（共 4 门）
中级
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
完成时间大约为15 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
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
授课大纲 - 您将从这门课程中学到什么
完成时间为 1 小时
Welcome to the Course!
Welcome to: Fundamentals of Reinforcement Learning, the first course in a four-part specialization on Reinforcement Learning brought to you by the University of Alberta, Onlea, and Coursera. In this pre-course module, you'll be introduced to your instructors, get a flavour of what the course has in store for you, and be given an in-depth roadmap to help make your journey through this specialization as smooth as possible.
完成时间为 1 小时
4 个视频 （总计 20 分钟）, 2 个阅读材料
完成时间为 4 小时
An Introduction to Sequential Decision-Making
For the first week of this course, you will learn how to understand the exploration-exploitation trade-off in sequential decision-making, implement incremental algorithms for estimating action-values, and compare the strengths and weaknesses to different algorithms for exploration. For this week’s graded assessment, you will implement and test an epsilon-greedy agent.
完成时间为 4 小时
8 个视频 （总计 46 分钟）, 3 个阅读材料, 2 个测验
完成时间为 3 小时
Markov Decision Processes
When you’re presented with a problem in industry, the first and most important step is to translate that problem into a Markov Decision Process (MDP). The quality of your solution depends heavily on how well you do this translation. This week, you will learn the definition of MDPs, you will understand goal-directed behavior and how this can be obtained from maximizing scalar rewards, and you will also understand the difference between episodic and continuing tasks. For this week’s graded assessment, you will create three example tasks of your own that fit into the MDP framework.
完成时间为 3 小时
7 个视频 （总计 36 分钟）, 2 个阅读材料, 2 个测验
完成时间为 3 小时
Value Functions & Bellman Equations
Once the problem is formulated as an MDP, finding the optimal policy is more efficient when using value functions. This week, you will learn the definition of policies and value functions, as well as Bellman equations, which is the key technology that all of our algorithms will use.
完成时间为 3 小时
9 个视频 （总计 56 分钟）, 3 个阅读材料, 2 个测验
完成时间为 4 小时
Dynamic Programming
This week, you will learn how to compute value functions and optimal policies, assuming you have the MDP model. You will implement dynamic programming to compute value functions and optimal policies and understand the utility of dynamic programming for industrial applications and problems. Further, you will learn about Generalized Policy Iteration as a common template for constructing algorithms that maximize reward. For this week’s graded assessment, you will implement an efficient dynamic programming agent in a simulated industrial control problem.
完成时间为 4 小时
10 个视频 （总计 72 分钟）, 3 个阅读材料, 2 个测验
审阅
- 5 stars81.90%
- 4 stars14.61%
- 3 stars2.45%
- 2 stars0.33%
- 1 star0.67%
来自FUNDAMENTALS OF REINFORCEMENT LEARNING的热门评论
由 CS 提供Feb 10, 2021
This is a relatively gentle introduction for the mathematically sophisticated, but does well to set the stage for the rest of the specialization and introduce the newcomer to the field.
由 AB 提供Sep 6, 2019
Concepts are bit hard, but it is nice if you undersand it well, espically the bellman and dynamic programming. Sometimes, visualizing the problem is hard, so need to thoroghly get prepared.
由 HC 提供Feb 18, 2020
Excellent and well done course on some of the basics of RL. Good mix of lectures, reading, quizzes and programming assignments. Also a good balance between pure theory and examples.
由 AA 提供Aug 16, 2021
This course is the best course for anyone who needs to enter into the field of RL. Content within the course is excellent and instructors have explained each and every topic very well.
关于 强化学习 专项课程
The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI).
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