Chevron Left
Back to Prediction and Control with Function Approximation

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

749 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


Apr 11, 2020

Difficult but excellent and impressing. Human being is incredible creating such ideas. This course shows a way to the state when all such ingenious ideas will be created by self learning algorithms.


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.

Filter by:

26 - 50 of 133 Reviews for Prediction and Control with Function Approximation

By Steven H

Jul 9, 2020

By Farhad A

Jun 9, 2020

By Chamani S

Feb 2, 2021

By Wojtek P

Apr 12, 2020

By Rafael B M

Sep 1, 2020

By Antonio C

Dec 2, 2019

By Sandesh J

Jun 25, 2020

By Jose M R F

Aug 14, 2020

By ding l

Jun 1, 2020

By Akash B

Nov 5, 2019

By Niju M N

Oct 24, 2020

By Christos P

Jan 19, 2020

By Jau-Jie Y

Jul 7, 2021

By Eric B

Nov 14, 2021

By Roberto M

Mar 29, 2020

By John J

Apr 28, 2020

By Sandro A

Jul 29, 2020

By Douglas D R M

May 21, 2021

By Casey S S

Feb 11, 2021

By Bhooshan V

Sep 3, 2021

By Kinal M

Jan 12, 2020

By Ivan S F

Nov 9, 2019

By Yingping Z

Jan 2, 2021

By Jicheng F

Jul 11, 2020

By Wahyu G

Mar 27, 2020