Reinforcement Learning

New Coursera specialization on RL

There is a new Coursera specialization on the fundamentals of reinforcement learning.

The specialization is taught out of University of Alberta by Dr. Adam White and Dr. Martha White, with guest lectures from many well known researchers and practitioners in the field. The specialization follows the Sutton Barto textbook from chapter 2 to 13 (give or take a few sections).

Right now, the first course is available. It goes from Bandits to Dynamic Programming and sets a foundation for more advanced topics in the field.

Anyways, go sign up and tell your friends :)

What is the best way to learn about Reinforcement Learning?

The best way to learn is with the online Reinforcement Learning specialization from Coursera and the University of Alberta. The two instructors, Martha and Adam White, are good colleagues of mine and did an excellent job creating this series of short courses last year. Also working to these course's advantage is that they are based on the second edition of Andy Barto's and my textbook Reinforcement Learning: An Introduction.

You can earn credit for the course or you can audit it for free (use the little audit link at the bottom of the Coursera form that invites you to "Start free trial"). Try signing up directly with coursera.org, then go here: https://www.coursera.org/specializations/reinforcement-learning

The RL textbook is available for free at http://www.incompleteideas.net/book/the-book.html.

If you want to gain a deeper understanding of machine learning and its role in artificial intelligence, then a good grasp of the fundamentals of reinforcement learning is essential. The first course of the reinforcement learning specialization begins today, June 14, so it is a great day to start learning about reinforcement learning!

RL specialization by U of Alberta (Richard Sutton) now on Coursera

Can only audit the first course in specialization because the other three are not on Coursera yet. But this should help I am guessing for those like me who had trouble reading the book. Also, a repo of code for the book I forked: Sutton book code

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Here is the course link: U of A RL specialization

Have you checked this one out?. It's there in my wishlist for a long time now 😅

https://www.coursera.org/specializations/reinforcement-learning

Thoughts on the RL coursera specialization by University of Alberta?

I heard about this course this morning and it looks like it could be good since I’m looking to learn RL. Has anyone taken it or have any opinions on it?

New Reinforcement Learning Specialization on coursera

[D] A good RL course/book?

I want to start learning RL. I have good knowledge about ML/DL, but RL is completely new to me. I want to build a RL model for an application. Since I know about ML/DL, I also know about Prob/Stats/Optimization, but only as a CS student. I come up with some courses:

CS234: CS234: Reinforcement Learning Winter 2021 (stanford.edu)

DeepMind (Hado Van Hasselt): Reinforcement Learning 1: Introduction to Reinforcement Learning - YouTube

Another DeepMind (David Silver): RL Course by David Silver - Lecture 1: Introduction to Reinforcement Learning - YouTube

UofA Coursera: https://www.coursera.org/specializations/reinforcement-learning

CS285: http://rail.eecs.berkeley.edu/deeprlcourse/

HSE Coursera: Practical Reinforcement Learning | Coursera

Due to limited time, I can only learn one course, but after that I can visit another one. What course should I start? There should be assignments too so that I can implement the code.

Extra: I also find some books about RL.

- Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series): Sutton, Richard S., Barto, Andrew G.: 9780262039246: Amazon.com: Books

- Reinforcement Learning: Industrial Applications of Intelligent Agents: D., Phil Winder Ph.: 9781098114831: Amazon.com: Books

- Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition: Lapan, Maxim: 9781838826994: Amazon.com: Books

If you can pick one, what will you pick?

https://www.coursera.org/specializations/reinforcement-learning courses in this specialization and Sutton Barto's book is the way to go.

There's a reinforcement learning specialization from the University of Alberta on Coursera. It's the university where Sutton's at and follows the book pretty closely. :)

https://www.coursera.org/specializations/reinforcement-learning?action=enroll

^this one?

you can also check the coursera for reinforcement learning specialization course. They cover this book thoroughly.

I would recommend the Coursera course on RL. The tutorial was supported by several coding exercises where you have to implement different agents. By googling "Coursera RL GitHub," you can find the solutions. However, I highly recommend the entire course.

This isn't an article but the coursera course on RL is awesome if you're new to the subject. It covers all the basics of Sutton and Barto (pretty much the introductory book on RL)

https://www.coursera.org/specializations/reinforcement-learning

There's also a 4 part Coursera specialization from the University of Alberta that uses the book:

https://www.coursera.org/specializations/reinforcement-learning

• Sutton & Barto
• David Silver's Lectures
• Alberta's RL Course: https://www.coursera.org/specializations/reinforcement-learning

Hey. Some great recommendations here so I will try to not repeat. Sutton and Barto book is a must and I would recommend this coursera specialization as your guide through it. https://www.coursera.org/specializations/reinforcement-learning

It follows the chapters of the book and it great to "reinforce" (couldn't resist) key topics. It also may provide a different lens to view a topic through when you are having a hard time digesting the book. If you are willing to pay for a certificate you can also have programming assignments. For me this was key but everyone has their own preference.

Sutton Barto if key to go through. So do not put it off.

As you go through it one other recommendation would be to create note-cards or an equivalent way to easily review key concepts as weeks go by.

After Sutton and Barto you should be able to digest most papers on RL topics. Slowly at first of course.

As others said I found David Silvers lectures incredible for intuition. I tried them before I finished the book and got not nearly as much out of them as afterwards.

I wouldn't say its hard but it definitely can take some time for it to click, or at least that was my experience. I would suggest Martha and Adams coursera course https://www.coursera.org/specializations/reinforcement-learning it goes over the first part of the book (plus a bit on function approximation) and provides another way to think about the problems. It is also used for the undergrad RL course at the u of a now. For the second part of the book I don't know of any online resources.

Coursera has a reinforcement learning specialization that follows Rich Sutton's textbook. It starts from the very beginning and develops out more advanced concepts building on each other.

https://www.coursera.org/specializations/reinforcement-learning?

(full disclosure I helped with the course)

The short answer is that reading 10 papers is very different from implementing an algorithm from a research paper.

IMHO, there's a couple of different ways to approach the problem.

1) You do online courses such as this one: https://www.coursera.org/specializations/reinforcement-learning#courses The benefit is that those are usually fairly good at increasing complexity gradually. The downside is it takes a while if you just want to learn DQN, for example.

2) You implement the simplest possible toy example first. For instance, instead of doing DQN, you start out by just doing Q learning. See for example here: https://www.learndatasci.com/tutorials/reinforcement-q-learning-scratch-python-openai-gym/

3) You read more papers, you also read the supplementary materials as they often include implementation details, you ask on stackoverflow if you get stuck on a particular thing, and you just plough on through.

You can of course always mix and match how you see fit.

Also keep in mind that papers are often not a very good manual for reimplementing an algorithm. For example, I was trying to implement a variant of k-means from scratch for a project of mine. I followed the description of the paper and never managed to get it to work properly. Then I looked on GitHub and actually found that other people also couldn't get it to work as described in the paper. Eventually I find the original source code on some abandoned website of the research group and voila turns out not in a million years would I have implemented it like that given the description of the paper. And now it just works.

Which RL course should I choose?

Hi!

Which set of courses would be the best option to learn RL and deep RL:

Option 1:

- Stanford's CS234 https://web.stanford.edu/class/cs234/

- And then followed up by Berkeley's Deep RL http://rail.eecs.berkeley.edu/deeprlcourse/

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Option 2:

- University of Alberta RL course: https://www.coursera.org/specializations/reinforcement-learning

- And then followed up by Berkeley's Deep RL http://rail.eecs.berkeley.edu/deeprlcourse/

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Option 3:

- MIT Professional course on RL: https://professionalonline1.mit.edu/reinforcement-learning

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Which one you would choose and why? Price for course is not a problem for me, only value of the content matters for me. Thanks in advance.

hello , i know one but i have no idea how good it is ( still didnt start in it, still in CNN )

https://www.coursera.org/specializations/reinforcement-learning

If you're looking to learn theory only and you're not interested in real world stuff, you may ignore my comment. Most probably I will get downvoted for this, there are no good sources for learning RL I tried every course out there including and not limited to:

• David Silver's course
• Coursera's reinforcement learning specialization
• Coursera's practical reinforcement learning

All of these courses will keep torturing you with cryptic mathematical formulas that unless you're interested in learning the mathematical foundations, are a waste of time to understand. You don't learn how to drive a car by learning how he expanding combustion gases push the piston, both are 2 completely different things. My advice is learn on a need-basis, look for whatever is necessary to solve the problem at hand and learn how it's done (if the problem requires the use of RL) otherwise, wait for Andrew NG to create a DRL course which I'm sure you don't want to miss.

From a financial standpoint, it probably isn't worth paying for a couple of extra courses and delaying getting a job for another semester. Maybe you'll use some of these topics in your career, maybe not, but by that logic you should take every single course offered just to be safe. IMO it's more important to show you're capable of learning (which the degree indicates) than it is that you're already familiar with specific concepts.

Personally, I'd advise to hurry up and get the diploma which will open up doors and make your resume better, start earning a salary and climbing up the career ladder earlier. If there are any topics that interest you, you can try auditing the courses later or find other MOOCs that teach these topics. For instance, here are MOOCs I quickly found for [HDDA],(https://online-learning.harvard.edu/course/data-analysis-life-sciences-4-high-dimensional-data-analysis?delta=1) optimization, and reinforcement learning.

What about this: https://www.coursera.org/specializations/reinforcement-learning

U of Alberta is where Sutton works: https://apps.ualberta.ca/directory/person/rsutton

These are my choices at the moment

Grokking Deep Reinforcement Learning

Deep Learning and the Game of Go

Reinforcement Learning Specialization - University of Alberta

I recommend the Reinforcement Learning Specialization from the University of Alberta. I'm still in the process of finishing it, but I've learn a lot from it. Very comprehensive!

As for the second question, I don't think I can provide you a good answer there. I do know that some robotics projects have RL integrated for experimentation (look at spotMicro and their RL section) but it's usually hard to implement due to the fact that its really hard to make simulation based learning effective and transferable to real life scenarios.

It's really nice going through the text book in parallel to this - https://www.coursera.org/specializations/reinforcement-learning

I am just auditing the course for now (making my way through resources I have found, eg, https://drive.google.com/drive/folders/0B3w765rOKuKANmxNbXdwaE1YU1k and https://drive.google.com/drive/folders/0B3w765rOKuKAMG9lbmRacFdsLWM) but I think I will unlock the assignments when I have more employment!

for those who wants to really get into the coding and theory:

https://www.coursera.org/specializations/reinforcement-learning comprehensive and understandable

https://github.com/dennybritz/reinforcement-learning after learning about the theory, there u can learnt the code and different algorithm

here is the classic and famous videos, but i personally will get lost easily.

https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ