
Reinforcement Learning and Optimal Control - iron0013
http://web.mit.edu/dimitrib/www/RLbook.html
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lawrenceyan
If you're looking for a great lecture course, I highly recommend CS 294. It's
taught by Sergey Levine, who is in my opinion one of the foundational
researchers at the frontier of deep reinforcement learning, and provides a
comprehensive overview on the current state of the art:
[http://rail.eecs.berkeley.edu/deeprlcourse/](http://rail.eecs.berkeley.edu/deeprlcourse/)

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najarvg
I took the time to look through two of the lectures from the slides available.
This is so inspirational. Thanks a lot!

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lawrenceyan
A link to all the lecture videos for Fall 2018 is available here if you'd like
to listen to the actual recordings:

[https://www.youtube.com/watch?v=opaBjK4TfLc&list=PLkFD6_40KJ...](https://www.youtube.com/watch?v=opaBjK4TfLc&list=PLkFD6_40KJIxJMR-j5A1mkxK26gh_qg37&index=25)

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dpandya
Not meant as a critique to this book, but the fact that RL-based approaches
rarely work for optimal control problems (in, for example, robotics) came as a
surprise to me, given the hype and focus on RL [1]. It turns out that model-
based methods for optimal control (e.g. linear quadratic control) invented
quite a long time ago dramatically outperform RL-based approaches in most
tasks and require multiple orders of magnitude less computational resources.
Maybe there's some hope for RL method if they "course correct" for simpler
control methods. At the moment, it seems like RL for robotics and control lies
to the side of "research" and not "engineering."

[1]: [https://www.alexirpan.com/2018/02/14/rl-
hard.html](https://www.alexirpan.com/2018/02/14/rl-hard.html)

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Jack000
for linear systems modern control theory is already optimal (?) so given a
linear system I'd assume an RL method would just approximate the optimal
control method. I think the potential for RL methods is in capturing
nonlinearities in actuators like artificial muscle.

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dpandya
I meant applying a linear approximation.

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clickok
Bertsekas' earlier books (Dynamic Programming and Optimal Control +
Neurodynamic Programming w/ Tsitsiklis) are great references and collect many
insights & results that you'd otherwise have to trawl the literature for. I'm
very interested to see what a book focused more narrowly on RL will be like--
Sutton's Introduction to Reinforcement Learning[0] is fantastic, but if you're
going to do research on RL, another text such as this one is necessary.

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

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samuell
Seems like an interesting complement to the (free) book on planning algorithms
by Steven Lavalle:
[http://planning.cs.uiuc.edu/](http://planning.cs.uiuc.edu/)

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del_operator
I liked the open courseware lectures with John Tsitsiklis and ended up with a
few books by Bertsekas: neuro dynamic programming and intro probability. Both
Bertsekas and Tsitsiklis recommended the Sutton and Barto intro book for an
intuitive overview. I liked it.

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joshuathomas096
This inspired me so much. Thank you for sharing!

