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Learner Reviews & Feedback for Prediction and Control with Function Approximation by University of Alberta

494 ratings
86 reviews

About the Course

In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem---function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment...

Top reviews


Jun 25, 2020

Surely a level-up from the previous courses. This course adds to and extends what has been learned in courses 1 & 2 to a greater sphere of real-world problems. Great job Prof. Adam and Martha!


Aug 14, 2020

Adam & Martha really make the walk through Sutton & Barto's book a real pleasure and easy to understand. The notebooks and the practice quizzes greatly help to consolidate the material.

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76 - 88 of 88 Reviews for Prediction and Control with Function Approximation

By Anton P

Apr 13, 2020

There is a lot of material covered in the course. Be aware the pace picks up considerably from the first two courses. This said, it is a worthwhile course to take.

By Vladyslav Y

Sep 08, 2020

I wish agents that are based on visual information (with the usage of CNN) would be included in the course. But overall that was really great!

By Sharang P

Feb 27, 2020

more detailed explanation of some of the assignments and how state values are got with tile coding but overall a great experience!

By Jerome b

Apr 09, 2020

Great course, based on the reference book about reinforcement learning. A must for anyone interested in machine learning.

By Rajesh M

Apr 17, 2020

I loved the course videos and programming assignments. The only suggestion would be to go a little deeper in the videos.


Aug 06, 2020

This was a good course but I really struggled to understand how each of the value functions translated into code.

By Rishabh K

May 20, 2020

The average reward and differential return needs to be explained more thoroughly


Oct 17, 2019

Course is great! Maybe some slides would be helpful not to forget.

By Prashant M

Jun 07, 2020

great course material but you need read the RL book through out the course. Also assignments are bit difficult, oops concept is mandatory.

By Justin N

Mar 31, 2020

Lectures are pretty good, but the programming exercises are extremely easy. All of the problems are rather contrived as well.

By Bakhti Y

May 04, 2020

I think It must be more deep neural networks dedicated course and not focus on coarse and tile coding!!!

By Bernard C

May 24, 2020

Course was good, but assignments were not well constructed. Problems with the unit tests were frequent.

By Vasileios V

Jul 11, 2020

Needs more work in my opinion. It's not bad of course. I just believe that more intuition should be built with better examples, outside the text book rather than going through the actual mathematical proofs