This course is part of the Reinforcement Learning Specialization

4.7
stars
585 ratings

Martha White +1 more instructor

17,607 already enrolled

Offered By

University of Alberta

Alberta Machine Intelligence Institute

Reinforcement Learning SpecializationUniversity of Alberta

About this Course

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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.
To be successful in this course, you will need to have completed Courses 1, 2, and 3 of this Specialization or the equivalent.

By the end of this course, you will be able to: 

Complete an RL solution to a problem, starting from problem formulation, appropriate algorithm selection and implementation and empirical study into the effectiveness of the solution.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 4 of 4 in the

Reinforcement Learning Specialization

Intermediate Level

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

Approx. 15 hours to complete

English

Skills you will gain

Artificial Intelligence (AI)
Machine Learning
Reinforcement Learning
Function Approximation
Intelligent Systems

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 4 of 4 in the

Reinforcement Learning Specialization

Intermediate Level

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

Approx. 15 hours to complete

English

Instructors

Instructor rating

4.82/5 (105 Ratings)

Martha White

Adam White

Offered by

University of Alberta

Alberta Machine Intelligence Institute

Syllabus - What you will learn from this course

Content Rating94%(1,562 ratings)

Week1

Week 1

1 hour to complete

Welcome to the Final Capstone Course!

1 hour to complete

2 videos (Total 10 min), 2 readings

Week2

Week 2

3 hours to complete

Milestone 1: Formalize Word Problem as MDP

3 hours to complete

4 videos (Total 23 min)

Week3

Week 3

1 hour to complete

Milestone 2: Choosing The Right Algorithm

1 hour to complete

7 videos (Total 40 min)

Week4

Week 4

1 hour to complete

Milestone 3: Identify Key Performance Parameters

1 hour to complete

4 videos (Total 25 min)

Reviews

4.7

123 reviews

5 stars
77.26%
4 stars
16.58%
3 stars
5.12%
2 stars
0.51%
1 star
0.51%

TOP REVIEWS FROM A COMPLETE REINFORCEMENT LEARNING SYSTEM (CAPSTONE)

by JSDec 5, 2019

This course changed my life! It was so good and I learned so much. I can't believe I'm now an astronaut. Next mission: go to Mars!

by SCApr 2, 2021

I give 4 stars because this last course is not as good as the previous ones. No real complaints, but it's not as "complete".

by YOApr 17, 2020

The project seems to be complicated at first glance, but the notebook will guide you through the implementation, and you will know what you are doing eventually.

by DLMay 31, 2020

Matha and Adam, thank you again. I will try to apply what I learned here to my own work, a content recommendation system based on deep learning and reinforcement learning.

View all reviews

About the Reinforcement Learning Specialization

The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI).

Harnessing the full potential of artificial intelligence requires adaptive learning systems. Learn how Reinforcement Learning (RL) solutions help solve real-world problems through trial-and-error interaction by implementing a complete RL solution from beginning to end. By the end of this Specialization, learners will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses or to apply AI tools and ideas to real-world problems. This content will focus on “small-scale” problems in order to understand the foundations of Reinforcement Learning, as taught by world-renowned experts at the University of Alberta, Faculty of Science. The tools learned in this Specialization can be applied to game development (AI), customer interaction (how a website interacts with customers), smart assistants, recommender systems, supply chain, industrial control, finance, oil & gas pipelines, industrial control systems, and more.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

A Complete Reinforcement Learning System (Capstone)

About this Course

Skills you will gain