University of Alberta
Fundamentals of Reinforcement Learning
University of Alberta

Fundamentals of Reinforcement Learning

This course is part of Reinforcement Learning Specialization

Taught in English

Some content may not be translated

Martha White
Adam White

Instructors: Martha White

87,033 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.8

(2,711 reviews)

|

92%

Intermediate level

Recommended experience

15 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • 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

Details to know

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Assessments

5 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.8

(2,711 reviews)

|

92%

Intermediate level

Recommended experience

15 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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This course is part of the Reinforcement Learning Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 5 modules in this 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.

What's included

4 videos2 readings1 discussion prompt

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.

What's included

8 videos3 readings1 quiz1 programming assignment1 discussion prompt2 plugins

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.

What's included

7 videos2 readings1 quiz1 peer review1 discussion prompt

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.

What's included

9 videos3 readings2 quizzes1 discussion prompt

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.

What's included

10 videos3 readings1 quiz1 programming assignment1 discussion prompt

Instructors

Instructor ratings
4.7 (778 ratings)
Martha White
University of Alberta
4 Courses92,011 learners
Adam White
University of Alberta
4 Courses92,011 learners

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4.8

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