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.
This course is part of the Reinforcement Learning Specialization
Offered By
About this Course
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
Skills you will gain
- Artificial Intelligence (AI)
- Machine Learning
- Reinforcement Learning
- Function Approximation
- Intelligent Systems
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
Syllabus - What you will learn from this course
Welcome to the Course!
On-policy Prediction with Approximation
Constructing Features for Prediction
Control with Approximation
Policy Gradient
Reviews
- 5 stars84.21%
- 4 stars12.86%
- 3 stars1.98%
- 2 stars0.66%
- 1 star0.26%
TOP REVIEWS FROM PREDICTION AND CONTROL WITH FUNCTION APPROXIMATION
Super interesting, challenging but the videos are very helpful to complement the understanding of the Sutton and Barto RL book. Thanks the Univ. of Alberta team!
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!
This specialization is a gift to humanity. It should have been inscribed into the golden disc of the Voyager and shared with the aliens.
Good course with a lot of technical information. I would add another assignment or make current ones a little bit more extensive, as there are many concepts to learn.
About the Reinforcement Learning Specialization

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