
A Long Peek into Reinforcement Learning - shry4ns
https://lilianweng.github.io/lil-log/2018/02/19/a-long-peek-into-reinforcement-learning.html
======
MasterScrat
I'm currently studying this topic (preparing an online course about it
actually) and here are the best resources I found so far:

Full Courses

\- From David Silver:
[http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)

\- From Yandex: [https://yandexdataschool.com/edu-
process/rl](https://yandexdataschool.com/edu-process/rl)

\- From Sergey Levine:
[http://rail.eecs.berkeley.edu/deeprlcoursesp17/index.html](http://rail.eecs.berkeley.edu/deeprlcoursesp17/index.html)

Articles:

\- Q-learning on Taxi-v2, very good basic explanation:
[https://www.learndatasci.com/tutorials/reinforcement-q-
learn...](https://www.learndatasci.com/tutorials/reinforcement-q-learning-
scratch-python-openai-gym/)

\- Q-learning and DQN, goes a bit further:
[https://neuro.cs.ut.ee/demystifying-deep-reinforcement-
learn...](https://neuro.cs.ut.ee/demystifying-deep-reinforcement-learning/)

\- "Pong from Pixels from Karpathy", introduces DQN and PG:
[https://karpathy.github.io/2016/05/31/rl/](https://karpathy.github.io/2016/05/31/rl/)

Baseline implementations:

\- "RL-Adventures", super clean Pytorch implementations:
[https://github.com/higgsfield/RL-Adventure](https://github.com/higgsfield/RL-
Adventure) (DQN) and [https://github.com/higgsfield/RL-
Adventure-2](https://github.com/higgsfield/RL-Adventure-2) (PG)

\- Repo for the Deep Reinforcement Learning Nanodegree:
[https://github.com/udacity/deep-reinforcement-
learning](https://github.com/udacity/deep-reinforcement-learning)

\- stable-baselines, a better documented fork of OpenAI baselines:
[https://github.com/hill-a/stable-baselines](https://github.com/hill-a/stable-
baselines)

If you have more high-quality resources please share! :D

~~~
richfnelson
I'm a TA for a RL class and would love to hear more about your online course.
If you have any content that's ready for viewing, that would be great.

------
AstralStorm
Cute, but insufficient to understand unless you already understand the
concepts and deep mathematics involved.

It doesn't tell you _why_ these methods work at learning so many problems. It
only tells you _how_ they work which is not enough and will bite you the
moment you fall into a pitfall. (and there are many more than mentioned)

And most importantly, it doesn't tell you how to construct working
fitness/target functions or why some generic ones work or don't, and when.

~~~
whymauri
>In this post, we are gonna briefly go over the field of Reinforcement
Learning (RL), from fundamental concepts to classic algorithms. Hopefully,
this review is helpful enough so that newbies would not get lost in
specialized terms and jargons while starting.

Read the mission statement at the very top. This isn't supposed to be Sutton
and Barto.

~~~
soVeryTired
"This isn't supposed to be Sutton and Barto"

It reads quite a lot like a synopsis of Section I of Sutton and Barto, down to
the ordering of the section headings. Not that the writing isn't clear and
concise, but it reads more like a cheat sheet than like serious exposition.

Sutton and Barto available below [0] for comparison. The google drive link
comes from Sutton's website [1] so I think it's kosher from a copyright
perspective.

[0]
[https://drive.google.com/file/d/1opPSz5AZ_kVa1uWOdOiveNiBFiE...](https://drive.google.com/file/d/1opPSz5AZ_kVa1uWOdOiveNiBFiEOHjkG/view)

[1] [http://incompleteideas.net/book/the-
book-2nd.html](http://incompleteideas.net/book/the-book-2nd.html)

------
minimaxir
Dupe:
[https://news.ycombinator.com/item?id=17924936](https://news.ycombinator.com/item?id=17924936)

