University of Alberta
A Complete Reinforcement Learning System (Capstone)
University of Alberta

A Complete Reinforcement Learning System (Capstone)

This course is part of Reinforcement Learning Specialization

Taught in English

Some content may not be translated

Martha White
Adam White

Instructors: Martha White

20,858 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.7

(622 reviews)

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94%

Intermediate level

Recommended experience

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

Details to know

Shareable certificate

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Assessments

2 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.7

(622 reviews)

|

94%

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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Build your subject-matter expertise

This course is part of the Reinforcement Learning Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 6 modules in this course

Welcome to the final capstone course of the Reinforcement Learning Specialization!!

What's included

2 videos2 readings1 discussion prompt

This week you will read a description of a problem, and translate it into an MDP. You will complete skeleton code for this environment, to obtain a complete MDP for use in this capstone project.

What's included

4 videos1 programming assignment

This week you will select from three algorithms, to learn a policy for the environment. You will reflect on and discuss the appropriateness of each algorithm for this environment.

What's included

7 videos1 quiz

This week you will identify key parameters that affect the performance of your agent. The goal is to understand the space of options, to later enable you to choose which parameter you will investigate in-depth for your agent.

What's included

4 videos1 quiz

This week, you will implement your agent using Expected Sarsa or Q-learning with RMSProp and Neural Networks. To use NNs, you will have to use a more careful stepsize selection strategy, which is why you will use RMSProp. You will also verify the correctness of your agent.

What's included

6 videos1 programming assignment

This week you will identify a parameter to study, for your agent. Once you select the parameter to study, we will provide you with a range of values and specific values for other parameters. You will write a script to run your agent and environment on the set of parameters, to determine performance across these parameters. You will gain insight into the impact of parameters on agent performance. You will also get to visualize the agents that you learn. Your parameter study will consist of an array of values that we will check for correctness.

What's included

6 videos1 programming assignment

Instructors

Instructor ratings
4.8 (111 ratings)
Martha White
University of Alberta
4 Courses93,119 learners
Adam White
University of Alberta
4 Courses93,119 learners

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