About this Course

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Intermediate Level

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

Approx. 30 hours to complete
English
Subtitles: English

Skills you will gain

Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level

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

Approx. 30 hours to complete
English
Subtitles: English

Offered by

University of Alberta logo

University of Alberta

Alberta Machine Intelligence Institute logo

Alberta Machine Intelligence Institute

Syllabus - What you will learn from this course

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Week
1

Week 1

1 hour to complete

Welcome to the Course!

1 hour to complete
2 videos (Total 12 min), 2 readings
2 videos
Meet your instructors!8m
2 readings
Read Me: Pre-requisites and Learning Objectives10m
Reinforcement Learning Textbook10m
6 hours to complete

On-policy Prediction with Approximation

6 hours to complete
13 videos (Total 69 min), 1 reading, 2 quizzes
13 videos
Generalization and Discrimination5m
Framing Value Estimation as Supervised Learning3m
The Value Error Objective4m
Introducing Gradient Descent7m
Gradient Monte for Policy Evaluation5m
State Aggregation with Monte Carlo7m
Semi-Gradient TD for Policy Evaluation3m
Comparing TD and Monte Carlo with State Aggregation4m
Doina Precup: Building Knowledge for AI Agents with Reinforcement Learning7m
The Linear TD Update3m
The True Objective for TD5m
Week 1 Summary4m
1 reading
Weekly Reading: On-policy Prediction with Approximation40m
1 practice exercise
On-policy Prediction with Approximation30m
Week
2

Week 2

8 hours to complete

Constructing Features for Prediction

8 hours to complete
11 videos (Total 52 min), 1 reading, 2 quizzes
11 videos
Generalization Properties of Coarse Coding5m
Tile Coding3m
Using Tile Coding in TD4m
What is a Neural Network?3m
Non-linear Approximation with Neural Networks4m
Deep Neural Networks3m
Gradient Descent for Training Neural Networks8m
Optimization Strategies for NNs4m
David Silver on Deep Learning + RL = AI?9m
Week 2 Review2m
1 reading
Weekly Reading: On-policy Prediction with Approximation II40m
1 practice exercise
Constructing Features for Prediction28m
Week
3

Week 3

8 hours to complete

Control with Approximation

8 hours to complete
7 videos (Total 41 min), 1 reading, 2 quizzes
7 videos
Episodic Sarsa in Mountain Car5m
Expected Sarsa with Function Approximation2m
Exploration under Function Approximation3m
Average Reward: A New Way of Formulating Control Problems10m
Satinder Singh on Intrinsic Rewards12m
Week 3 Review2m
1 reading
Weekly Reading: On-policy Control with Approximation40m
1 practice exercise
Control with Approximation40m
Week
4

Week 4

6 hours to complete

Policy Gradient

6 hours to complete
11 videos (Total 55 min), 1 reading, 2 quizzes
11 videos
Advantages of Policy Parameterization5m
The Objective for Learning Policies5m
The Policy Gradient Theorem5m
Estimating the Policy Gradient4m
Actor-Critic Algorithm5m
Actor-Critic with Softmax Policies3m
Demonstration with Actor-Critic6m
Gaussian Policies for Continuous Actions7m
Week 4 Summary3m
Congratulations! Course 4 Preview2m
1 reading
Weekly Reading: Policy Gradient Methods40m
1 practice exercise
Policy Gradient Methods45m

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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....
Reinforcement Learning

Frequently Asked Questions

  • 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.
  • 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.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

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