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. 22 hours to complete
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. 22 hours to complete
English

Offered by

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University of Alberta

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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
5 hours to complete

On-policy Prediction with Approximation

5 hours to complete
13 videos (Total 69 min), 2 readings, 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
2 readings
Module 1 Learning Objectives10m
Weekly Reading: On-policy Prediction with Approximation40m
1 practice exercise
On-policy Prediction with Approximation30m
Week
2

Week 2

5 hours to complete

Constructing Features for Prediction

5 hours to complete
11 videos (Total 52 min), 2 readings, 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
2 readings
Module 2 Learning Objectives10m
Weekly Reading: On-policy Prediction with Approximation II40m
1 practice exercise
Constructing Features for Prediction28m
Week
3

Week 3

6 hours to complete

Control with Approximation

6 hours to complete
7 videos (Total 41 min), 2 readings, 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
2 readings
Module 3 Learning Objectives10m
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), 2 readings, 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
2 readings
Module 4 Learning Objectives10m
Weekly Reading: Policy Gradient Methods40m
1 practice exercise
Policy Gradient Methods45m

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About the Reinforcement Learning Specialization

Reinforcement Learning

Frequently Asked Questions

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