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

753 ratings

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


Dec 1, 2019

Well peaced and thoughtfully explained course. Highly recommended for anyone willing to set solid grounding in Reinforcement Learning. Thank you Coursera and Univ. of Alberta for the masterclass.


Jun 24, 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!

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76 - 100 of 133 Reviews for Prediction and Control with Function Approximation

By Ola D

Jun 15, 2022

Fantastic course with fantastic instructors

By İbrahim Y

Oct 5, 2020

the course is the intro for high level RL


Jan 23, 2021

perfect and thank you for this course

By Teresa Y B

May 11, 2020

Very Useful and Highly Recommend !!!

By Stewart A

Oct 31, 2019

Simply the best course on this topic.

By Farzad E b

Aug 4, 2022

It was perfect, I really enjoyed it

By Junchao

May 29, 2020

Very good and self-oriented course!

By Fernando A S G

Mar 26, 2021

Excellent course! Thanks a lot!

By Wei J

Oct 11, 2020

It is a very perfect RL course.

By Antonis S

May 30, 2020

Really a well-prepared course!

By Ignacio O

Nov 29, 2019

Really good, I learned a lot.


May 2, 2020

Great speakers and content!

By Majd W

Feb 1, 2020

Very practical course.

By 李谨杰

Jun 17, 2020

Excellent class !!!

By Mohamed A

Sep 11, 2021

very good course

By Hugo T K

Aug 18, 2020

Excellent course.

By Murtaza K B

Apr 25, 2020

Excellent course

By Ivan M

Aug 30, 2020

Just brilliant

By Juan “ L

Aug 3, 2022

great course!

By Oriol A L

Nov 19, 2020

Very good!

By Cheuk L Y

Jul 8, 2020

Very good!

By Jialong F

Feb 23, 2021


By Justin O

May 18, 2021


By Artod

Feb 27, 2021


By Ananthapadmanaban, J

Jul 19, 2020

I am disappointed with policy gradients being introduced on last week of the 3rd course. The instructors need to understand that 12 weeks is too much for introduction before starting a good project to implement the concepts with a hope to better understand them (course 4). Policy gradients should have been introduced in week 3/4 of course 2 itself. The content before that should be made more efficient (4 weeks to understand until q-learning/sarsa and 2 weeks to understand function approximation should be enough). I realized after course 2 that Andrew Ng has 3/4 videos on RL in the recently released ML class from Stanford. I am yet to go through them, but I feel they may explain these faster with same amount of rigour. However, the stanford class assignments are not public, which makes this course still useful because of the assignments. However, thanks to the instructors for this course.