About this Course
65,724 recent views

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. 19 hours to complete

Suggested: 4-6 hours/week...

English

Subtitles: English

Skills you will gain

Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems

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. 19 hours to complete

Suggested: 4-6 hours/week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
1 hour to complete

Welcome to the Course!

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

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
8 hours to complete

Constructing Features for Prediction

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
8 hours to complete

Control with Approximation

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
6 hours to complete

Policy Gradient

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
4.8
14 ReviewsChevron Right

Top reviews from Prediction and Control with Function Approximation

By ABNov 5th 2019

Great Learning, the best part was the Actor-Critic algorithm for a small pendulum swing task all from stratch using RLGLue library. Love to learn how experimentation in RL works.

By IFNov 10th 2019

Great course. Slightly more complex than courses 1 and 2, but a huge improvement in terms of applicability to real-world situations.

Instructors

Avatar

Martha White

Assistant Professor
Computing Science
Avatar

Adam White

Assistant Professor
Computing Science

About University of Alberta

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....

About 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 research institute that pushes the bounds of academic knowledge and guides business understanding of artificial intelligence and machine learning....

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

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • 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.

More questions? Visit the Learner Help Center.