Practical Reinforcement Learning
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Welcome to the Reinforcement Learning online course. Here you will find out about: - foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. --- with math & batteries included - using deep neural networks for RL tasks --- also known as "the hype train" - state of the art RL algorithms --- and how to apply duct tape to them for practical problems. - and, of course, teaching your neural network to play games --- because that's what everyone thinks RL is about. We'll also use it for seq2seq and contextual bandits. Jump in. It's gonna be fun! Do you have technical problems? Write to us: [email protected].Get a Reminder
| Rating | 3.8★ based on 83 ratings |
|---|---|
| Length | 7 weeks |
| Effort | 6 weeks of study, 3-6 hours/week for base track, 6-9 with all the horrors of honors section |
| Starts | Jan 31 (117 weeks ago) |
| Cost | $49 |
| From | Higher School of Economics, National Research University Higher School of Economics, HSE University via Coursera |
| Instructors | Pavel Shvechikov, Alexander Panin |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Programming Data Science |
| Tags | Computer Science Data Science Algorithms Machine Learning |
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What people are saying
reinforcement learning
Best course so far on reinforcement learning.
This is one of the Best Course available on Reinforcement Learning.
I will happily continue to the next course in this specialization :) Brilliant content but quite some bugs in assignments Course 4 of Advanced Machine Learning, Practical Reinforcement Learning, is harder than Course 1, Introduction to Deep Learning.
A good reason for taking this course is because it is one of few online courses where you can play with actual programming exercises of various reinforcement learning techniques, from dynamic programming to deep Q networks and actor critiques.
great course and fabulous exercises It still a beta:( Indeed, this the 1st reinforcement learning course during May 2018.
Content provides a good - and useful :) - overview of reinforcement learning.
I am convinced, that programming practices make it the best course on reinforcement learning currently available.
An amazing introduction to the state of the art in deep reinforcement learning, with plenty of practical exercises.
This course provides a very good foundation for understanding modern reinforcement learning algorithms and very recent articles.
Although several homework is in beta, the course covers extensive reinforcement learning algorithms.
Good Good Introduction to Reinforcement Learning I've done about 14 courses on coursera and this was the worst.
Instead of the videos it's easier to just read a book on reinforcement learning.
This is really a good course to deeply understand and apply the reinforcement learning.
Nice introduction into Reinforcement Learning.
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hard to understand
Instructors are hard to understand.
Although still in its beta version this course is a comprehensive introduction to reinforcement learning.It doeshas some bugs in submission and in assignment code which I hope will be dealt with in future versions Sometimes it is hard to understand/follow the instructors.
The teachers are very hard to understand.
THX Instructor talks to fast and is hard to understand.
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fix the bugs
I had to download the docker container locally and fix the bugs in order to submit.
Wonderful content and super interesting problem assignments, but please fix the bugs in the graders and spend some time to adapt the code to the Coursera platform.
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discussion forum
BUT, at the moment the course is extremely raw: 1) For larger/longer assignment, it is impossible to work with coursera notebooks (keep disconnecting); It takes lost of efforts to set-up own environment (and you shouldn't really count on discussion forum for help).
Also, the discussion forum is very helpful and I can usually get out of stuck by following mentors' and other students' advice.
-Great exercise templates with interesting applications of RL algorithms.-There are always references to good papers and new developments in RL.-Good sense of humor in the lecture and templates.-The discussion forum addresses the the bugs of the course.-The course is challenging in the right level.
Also, most of the bugs and problems are already solved by the community, you just need to visit the Discussion forums to find the solutions.
The support in the discussion forums is the main area where it lacks.
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take this course
But , The worst part is if you take this course you will be all on your own and no body help you out as TA .
If you want to challenge yourself and solve really interesting problems, take this course.
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highly recommend
If they fix the course, this course will be highly recommended.
I highly recommend it!
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andrew ng
I think this course can be what Andrew Ng's course is for machine learning to Reinforcement learning.
Maybe Andrew Ng courses or Python Course or Advanced ML course on google cloud (GCD ) spoiled me However statistically and self-judgement , this is not the case.
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open ai
Examples are mostly for environments of Open AI gym.
You will implement algorithms like cross entropy method to DQNs and A3C .Assignments uses Open AI Gym so you can get some good practical results .Overall I loved this course and would like to recommend to any one who is getting started in Reinforcement Learning.Thank you.
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alexander panin
Great thanks to Pavel Shvechikov and Alexander Panin for making such a useful course available!
Get ready to have some "fun" (and by "fun" I mean the opposite of "fun") taking these quizzes.Third reason is, Alexander Panin can occasionally be difficult to understand in English (that's as gently as I can put it).
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other courses
Fortunately some early takers of the class left helpful comments on the forum, with which you can solve the most of issues if you read them carefully.Quality of presentation is not as good as other courses I found in the Coursera.
The pace is quick and the assignment is challenging sometimes The course is dense and is accompanied by quality support (references to other courses, articles,...).
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programming exercises
The programming exercises were not well explained.
I still gave it two stars because the programming exercises were interesting and usefull.
so much
He "killed" so much interesting material in this course.
I loved this course, many things I have revisit to get a complete and thorough understanding, there is so much happening and so much to learn, and this course certainly showed me the possibilities.
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| Rating | 3.8★ based on 83 ratings |
|---|---|
| Length | 7 weeks |
| Effort | 6 weeks of study, 3-6 hours/week for base track, 6-9 with all the horrors of honors section |
| Starts | Jan 31 (117 weeks ago) |
| Cost | $49 |
| From | Higher School of Economics, National Research University Higher School of Economics, HSE University via Coursera |
| Instructors | Pavel Shvechikov, Alexander Panin |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Programming Data Science |
| Tags | Computer Science Data Science Algorithms Machine Learning |
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