Martha White
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 …
Instructor Details
Courses : 4
Specification: Prediction and Control with Function Approximation
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34 reviews for Prediction and Control with Function Approximation
Add a review
| Price | Free |
|---|---|
| Provider | |
| Duration | 18 hours |
| Year | 2019 |
| Level | Intermediate |
| Language | English |
| Certificate | Yes |
| Quizzes | No |
FREE
Luiz C –
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
Chang, W C –
The course presentation is wonderful. I can’t stop after I watch the first video.
Navid H –
The material is very good. But this course needs better instructors/ method of teaching. The book is also written in an unnecessarily technical way filled with jargon. explanations are not clear, simple stuff is presented in a very complicated manner for no obvious way.
RJT –
Course is great! Maybe some slides would be helpful not to forget.
Mark J –
This, the third in an exceptionally well–paced series of four courses on Reinforcement Learning, extends the scope of the subject to include parameterized functions (i.e., neural networks). The section on tiling methods is especially interesting. The course is taught under the auspices of professors who, quite literally, wrote the book on reinforcement learning, and includes several video lectures by leading practitioners and theorists in the field. The final programming assignment, in particular, made me feel like I did when I wrote my first computer program that actually did what it was supposed to way back when –– delight and amazement.
Mateusz K –
Its got a great variety of very applicable examples, use cases, and assignments. May be tough if people don’t quite understand how neural networks work, so I suggest having a basic understanding of NN for parts of this course.
Stewart A –
Simply the best course on this topic.
Max C –
I had a much better experience with the autograder than in course 2.
Akash B –
Great Learning, the best part was the Actor–Critic algorithm for a small pendulum swing task all from stratch using RLGLue library. Love to learn how experimentation in RL works.
Ivan S F –
Great course. Slightly more complex than courses 1 and 2, but a huge improvement in terms of applicability to real–world situations.
Julien T –
Great course and specialization. The teachers are great, the material well presented and balanced. I strongly recommend this course to anyone interested in the field of Reinforcement Learning. For maximum chance of success I suggest following all 3 courses in succession and investing the necessary amount of time to read the textbook chapters as specified at the beginning of each week. Looking forward to completing the capstone project now!
Ignacio O –
Really good, I learned a lot.
Antonio C –
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.
Sebastian P B –
This was a very good and though course. The content in this course is perfect to get yourself the necessary bases in order to start getting into deep RL. It doesn’t really explain that far, but at the end you will have a basic idea of how deep learning can be used with RL. Enough to start reading papers about it or to watch other lectures focused on that topic.
Walter O A –
An almost overwhelming amount of material, however we managed to navigate through the thicket. The labs were well maintained and provided robust tests so that one could have a high degree of confidence in the solution before submitting to the grader. I really appreciate this. I would recommend this course to anybody wanting a serious introduction to reinforcement learning.
Alexander P –
Great course on more advanced reinforcement learning techniques. Can’t wait to apply these new skills in the wild.
Raktim P –
Great Course! Highly recommended for beginners.
Kaustubh S –
It was a wonderful course. To the point yet well–explained concepts.
Pachi C –
Fantastic course and great content and teachers!!!
David R –
Excellent course. The videos, quizzes, and especially the exercises add a lot of extra value to the text book (which is available for free – Sutton and Burto, 2nd edition). Of course it is not perfect – the videos are sometimes a bit dry, the NN part was brushed over too quickly for a beginner (luckily I had taken some courses about deep learning, so I was ok – but if you don’t know the basics of NN, week 2 might be quite challenging for you). Other than that the biggest disadvantage is that the course forums are still quite empty – and so if you get stuck you can be on your own… But you shouldn’t get stuck, and I guess this will improve over time.
Dmitry S –
Definitely a course to take to learn the ropes of RL. For this course, it is critical to follow and math. I’d love to give 5 stars to this course but will however take one away since the course could benefit a lot if the math was made a bit simpler to follow. The book referenced in the course is excellent and does help, but still, some more pedagogical repetition/rephrase, simplification of notation, a bit slower pace of narration would make the course even better. Having said that, this seems to be the best course available at this time. Many thanks to tutors.
Kinal M –
A great and interactive course to learn about using function approximation for control. Great way to learn DRL and its alternatives.
Hugo V –
it was great to apply what I have learned from the book, but it was hard to find my mistakes in the course 3 notebook. I also misunderstood the alphas in the course 4 notebook at first glance, their indices look like they are powers (sorry for the bad english). Besides it, great course.
Christos P –
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.
Lik M C –
The course is still good. But the assignment is not as good as course 1 and 2. In fact, the contents of the course are getting complicated and interesting as well. But the assignments are relatively simple.
LOS –
Great course, deserve 5 stars. It is a good complement to the book, it adds interesting visualizations to help parse the content. The only issues were in the exercises. There are technical issues with the notebook platform where it keeps disconnecting from time to time, with no warning, and you lose your unsaved work (seems like token expiration).
Maxim V –
Good content, but there was a highly unpleasant surprise in the programming assignments, namely this: “Retakes: You can attempt this assignment 5 times every 4 months.” First of all, this is a highly unusual and therefore unexpected requirement on Coursera. Also, considering how buggy graders are and that some assignments require submitting results separately from the notebook, this is a really high risk of having to wait 4 months for another chance.
LIWANGZHI –
Everything is amazing in this course! Dont miss it!
Andrew G –
Did a good job of attaching a programming assignment to each lesson and giving clear and detailed instructions throughout
Majd W –
Very practical course.
Gordon L W C –
The course is very comprehensive on the content. But I think the difficulty of this course is in some sense too high for most people who don’t have a background in engineering degree due to the extensive use of advanced mathematics. I think it might be a better idea if you are focusing on a few critical algorithms that trying to cover too much algorithms which is quite overwhelming
Tri W G –
Give nive theoretical foundation. I found RL courses are abstract, but the programming assignment give a nice conceptualization.
Mukund C –
Excellent Course and Lectures. Loved it!! So important to read the chapters in the book ahead of time. Book is also excellent!! I liked the way the instructors explained the equations and broke them down. Nicely done!! I wish some more of the questions in the quiz reflected the data structures we use in the programming exercise, which will be super helpful to reinforce the concepts when we do the programming exercises. In other words, an intermediate step of a worked example between the Pseudo Code Algorithm in the Texbook/Lectures and the Programming Exercise. For example, more of the Feature Vector > Action Value Calculation even if we have to do some matrix manipulation by hand, that’d be wonderful. One of the quizzes has something like that (but more simplified) which was perfect.
Roberto M –
I found the course quite tough but really interesting. I would say that reading the book’s chapters more than once is necessary to optimally grasp the concepts.