Reinforcement Learning

Best video tutorial series for learning Reinforcement Learning for practical usage

I want to learn Reinforcement Learning with some video series, with more on the practical usage side instead of more on the theoretical side. These are some of the series, people have recommended to me:

Georgia Tech’s “Reinforcement Learning” course on Udacity

Microsoft's "Reinforcement Learning Explained" on edX

University of Alabama's "Fundamentals of Reinforcement Learning" on Coursera (released a week ago)

Though I am new to reinforcement learning, I would prefer the course to be a bit advanced (slow courses take away my interest). Which one should I do?

List Of Free Reinforcement Learning Courses/Resources Online

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1. Reinforcement Learning Offered at Georgia Tech as CS 8803
2. Practical Reinforcement Learning
3. Reinforcement Learning Explained
4. Reinforcement Learning in Finance
5. Introduction to reinforcement learning
6. Deep Reinforcement Learning CS 294-112 at UC Berkeley
7. An introduction to Reinforcement Learning (Medium Article)
8.  Introduction to RL and Immediate RL
9. Introduction to RL

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I'm seeing many more moderate to advanced courses on udacity/udemy/coursera actually.

e.g. https://www.udacity.com/course/reinforcement-learning--ud600

Also, while you might not find a full course online, there are many videos of single lectures geared toward researchers/grad students.

https://www.udacity.com/course/reinforcement-learning--ud600

I'm not a game developer. I'm more a Machine Learning guy and I stopped by since I found your question interesting. However, after reading it I thought I might make a few suggestions about the ML parts

I'm not sure how Genetic Algorithms are used in games. Neural Networks are definitely used as it is a broad family of algorithms and you really don't want to dive into the whole thing.

Assuming that you are familiar with ML algorithms I would recommend reading about Reinforcement Learning algorithms as it is probably more suitable for a competitive multi agent environment that agents can learn from playing against each other

If you are not familiar with ML yet, start of with something more broader. Maybe the Machine Learing course by Andrew Ng from http://coursera.com which is free too. Then after that move on to algorithms in RL. This can be a good start: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow

also this seems like a nice course: https://www.udacity.com/course/reinforcement-learning--ud600

Well, now we need a Reinforcement Learning, best one I found was on Udacity(intro and full course)... yet these courses aren't as well "organized" as the way Andrew Ng can teach those topics.

More of a broad look at AI, but I found the reading list from Berkeley's Center for Human-Compatible AI helpful when starting out. Probably a bit out of date, but well-organized, and you can skip the stuff not relevant to you.

Udacity's ML course and Beginner RL course are free and more hands-on Python, and I generally like the instructors (as teachers and researchers). They also have a more in-depth deep RL course which is not free.

Possibly ML spec, you can glance through the machine learning, reinforcement learning, cv lectures to see if it's mathy enough for you:

• https://www.udacity.com/course/machine-learning--ud262
• https://www.udacity.com/course/reinforcement-learning--ud600
• https://www.udacity.com/course/introduction-to-computer-vision--ud810

I've found that mainstream RL papers tend to be pretty consistent in definitions, but that the main references (Bertsekas, Puterman) deviate in notation since they are founded in control/OR. I don't know of any single reference that is appropriate, but I might suggest looking through several papers that contain RL proofs to get a general feel. These should have definitions with the clarity that you're looking for (or references to the texts, so that you can match up notation). Perhaps (for simple proofs):

• Gordon 1995 (clear definitions IIRC): http://aima.eecs.berkeley.edu/~russell/classes/cs294/s11/readings/Gordon:1995.pdf [actually, the reading list this is from is probably a good list to glance through: http://aima.eecs.berkeley.edu/~russell/classes/cs294/s11/readings/]

• Ng 99 (reward shaping, simple proof): http://robotics.stanford.edu/~ang/papers/shaping-icml99.pdf

• Ng/Russell 2000 (IRL, simple proof): http://ai.stanford.edu/~ang/papers/icml00-irl.pdf

• Uhh https://arxiv.org/abs/1606.05312? https://arxiv.org/pdf/1206.3285.pdf? Depends what kind of theorems you want to prove... policy gradients? options? off-policy stuff?

• Looking for more intense stuff? Maybe https://link.springer.com/content/pdf/10.1023/A:1007678930559.pdf?

Also, are you using the latest Sutton? It has a lot more meat than the 98 version (but I agree with the general sentiment): http://incompleteideas.net/book/.

But also, Puterman and Bertsekas are good references and often referenced, even if notation is different.

Also, if you like video lectures, Littman works several proofs in this course: https://www.udacity.com/course/reinforcement-learning--ud600 (they aren't the absolute clearest / perfect, but it does provide the broad strokes in more detail than Sutton I think; e.g., for why Q-learning converges).