Prediction and Control with Function Approximation
University of Alberta and Alberta Machine Intelligence Institute via Coursera
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Overview
Class Central Tips
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
Taught by
Martha White and Adam White
Charts
- #2 in Subjects / Machine Learning / Supervised Learning
- #2 in Subjects / Machine Learning / Reinforcement Learning
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Reviews
4.8 rating, based on 13 reviews
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Anonymous completed this course.
I really enjoyed this class. A mind blowing tour of the main algorithms used for continuous online use cases. Very clearly articulated lectures. Big congrats to Martha and Adam! -
Anonymous completed this course.
The course is very concise and to the point. It covers all the necessary aspects and tries its best to be in sync with the reinforcement learning book by Sutton. The instructors are well experienced and know how to present an idea in an easy but elegant manner. The weekly quiz and projects are challenging and will surely test the reader's understanding of the course. If you are new to reinforcement learning, I would really recommend this course along with it's two other courses before this in the Reinforcement Learning Specialization by University of Alberta. All in all you will have a great time learning this course. -
Luiz Cunha completed this course, spending 1 hours a week on it and found the course difficulty to be medium.
Almost perfect, except two ~minor objections:
1/ the learning content between the 4 weeks is quite unbalanced. The initial weeks of the course are well sized, whereas week #3 and week #4 feel a touch light. It feels like the Instructors rushed to make the Course available online, and didn't have time to put as much content as they wished in the last weeks of the Course
2/ there are too many typos in some notebooks (specifically notebook of week #3). It gives the impression it was made in a rush, and nobody read over it again. Besides there seems to currently be some issue with this assignment -
Anonymous completed this course.
Definitely a course to take to learn the ropes of RL. For this course, it is critical to follow the math. 4 stars instead of 5 only because the math could be made easier to follow with some extra effort from the tutors. But if you're strong in math, you should be fine. The math itself is not difficult, but the notation is challenging and the terminology is a bit tough to keep in head. -
Jose Marcos Rodríguez Fernández completed this course, spending 3 hours a week on it and found the course difficulty to be medium.
The instructors do a great job summarizing and being concise while following Sutton & Barto's RL introduction book.
The programming exercises, done via jupyter notebooks, really help to consolidate the theoretical knowledge acquired during the lessons and by reading the book.
Highly recommended course for anyone interested in getting a practical introduction to RL algorithms. -
Anonymous completed this course.
This course is very rich of both mathematical and practical concepts, and it actually provides you with powerful tools to understand and use Reinforcement Learning. So far, it is the most interesting course in this specialization. Lectures are very clear and they often explain more deeply some concepts you find in the text book. Quizzes are challenging and well constructed. -
Anonymous completed this course.
I really enjoyed this third course of the specialisation.
The content and explanations are very helpful in building your intuition around quite complex concepts of RL with approximation. Quizzes and programming exercises are challenging enough to help you grasp necessary concepts and get hands on experience. Look forward to the next course in the specialisation. -
I really enjoyed taking this course and learned a lot. The Reinforcement Learning Specialization (https://www.coursera.org/specializations/reinforcement-learning) is a great introduction to reinforcement learning. This course is the third one in the specialization. All programming assignments are in Python.
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Anonymous completed this course.
This course covers a wide variety of topics and dives a good amount into each of them.
I wish the instructors would cover some of the topic and the math in a little more detail, and some of the content seems a tiny bit rushed, but otherwise, a brilliant course overall. -
Really engaging and interesting course. Amazingly talented instructors and equally amazing content. A must for those who are learning reinforcement learning or those who want to expand their knowledge in the field.
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Anonymous completed this course.
Amazing course with amazing, intuitive visualizations. It is clear that the instructors have spent a lot of time and effort in trying to make the course as visually descriptive as possible. -
Anonymous completed this course.
Nice course that is part of 3 more courses. All of the together cover a wide area in RL. Beginner in maths can easily follow. It's good to know some python before you start (very basic level) -
The lectures are really dense. You have to slow down and watch multiple times in conjunction with the book to really get it.
Very abstract but the coding assignments are helpful.