Reinforcement Learning:
Math background(수학 기본):
Out of all the important branches of math commonly recommended to learn machine learning in general (prob, stats, linear algebra, optimization, calculus), probability and calculus are the most important. The former is because value function at the end of the day is just repeated application of total probability theorem and total expectation theorem. The latter is because model free control algorithms (MC, TD, and their families) need gradient based methods to update parameters.
Going through the first 12 lectures of this course(and please, actually do the problems) will give you a great probability foundation. MIT OCW in general is great for foundational math/science background. They have courses on a single variable and multivariate calculus as well.
If you want to get deep into the derivation of some SOTA policy gradient methods using function approximation, then linear algebra and optimization are definitely required. Again, refer to MIT OCW for linear algebra. I am not too sure about optimization resources myself, but I know Boyd's convex optimization course is great.
University Lecture Courses(대학 강의)
There are three amazing lecture resources for learning RL. They all cover most if not every aspect of the field, but each has its focus.
- David Silver's UCL RL Lectures focuses more on traditional planning/DP methods, such as policy/value iteration, MC, TD, bandit problem. The lecturer comes from a game playing background. His lecture closely mirrors Sutton & Barto's RL textbook. It's THE textbook in the field, and I recommend it. The lecturer also worked on AlphaGo, so he has a few lectures highlighting that as well which was pretty cool. He does not go over the frontiers of RL research much. (Inverse RL, Meta-learning, etc)
- Berkeley's RL class, taught by Sergey Levine, focuses on more modern RL methods which heavily uses deep neural nets as a function approximator. Naturally, policy gradient based methods are given emphasis, and a lot of cutting edge research topics are covered. Lecturer comes from a robotics background.
- Stanford's RL class taught by Emma Brunskill, is a nice balance between the two, and is the one that I recommend the most for beginners. Does a great job covering foundational RL like Silver, but also covers modern methods like Levine. Has some lecturers covering frontier research, but not as much as Levine's class. Lecturer comes from a more diverse background: healthcare, education, etc.
from raichet at reddit
Video Lecture(인터넷 강의)
- Deep RL Bootcamp
- DeepMind: Introduction to reinforcement Learning