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New Draft of “Reinforcement Learning: An Introduction, Second Edition” (dropbox.com)
170 points by Gimpei on Sept 23, 2016 | hide | past | favorite | 33 comments

The submitted link is no longer working:

> Dropbox Error (429) This account's links are generating too much traffic and have been temporarily disabled!

I've uploaded a mirror of the PDF:


If you can, leave the tab open for a while to keep seeding the file! (It's a torrent.)

Looks like the author has a new URL for the PDF, hosted by the university:


Since it is a draft, the author's new link is the right one to read, since the torrent I posted might be out of date by the time you read this.

Thank you sir!!

Thanks a lot !


If anyone is interested in exploring this, I highly recommend skipping to the blackjack exercise. This isn't to say you should read the book fully but I feel blackjack is simple and enough to get you addicted. Starting with Monte Carlo sampling is quite approachable and, once you've done that, extending it is relatively easy.

I also highly recommend reading about Edward O. Thorp[1]. He was a friend of Claude Shannon, whom he frequented Las Vegas with, and used the IBM 704 (first mass produced computer with floating point ops) to explore blackjack game theory in ~1956. To apply his research he borrowed $10,000 from someone with mob connections and won $11,000 in a single weekend. He also developed the first wearable computer (for a specific definition of computer).

[1]: https://en.wikipedia.org/wiki/Edward_O._Thorp

Wonderful! I took his machine learning coarse at the University of Alberta and we used the first edition in his class - though he didn't make it mandatory to buy his book. In any case, it was well worth the asking price. The math isn't too hard and progresses gently enough. I think the lisp code he wrote for the first edition is online somewhere. I'll have to check his web presence.

The (mostly) lisp code from the first edition can be found here: https://webdocs.cs.ualberta.ca/~sutton/book/code/code.html

Sweet, this is like finding out that the Bible has been revised and updated. Except that I immediately went and read this.

Sutton & Barto is the Bible for RL. Having it be freely available updated is wonderful!

Here is the draft linked from the author's website:


just reading page 15, arg max = maximal ..., I think that global maximum or local maximum is better than maximal.

I would like to read all the interesting fruit of RL in just one hour, can someone suggest a short book for someone with advanced maths skills?

Thanks a lot to the authors the book seems to be really interesting.

Edit: In page 25, an extended example: tic-tac-toe the rule to update the value of each state v(s)=v(s)+a(v(s')-v(s)) doesn't take into account that if in s' there is a winning strategy by the policy then previous values is also part of a winning strategy. So if v(s')=1 (win) then v(s)=1 (I can win). In my very humble opinion, the author should digress a title to talk about this very important point.

The book is hundreds of pages long, if he diverges to talk about everything in Chapter 1 it would be a mess.

The scenario you describe is if alhpa=1, and it would do poorly. Try thinking about games where the opponent doesn't play an optimal game. Try thinking of stochastic environments.

What I suggest is to use the function: if v(s')==1 then 1 else the usual rule.

Lets pretend alpha = 1 on a win and alpha = 0.1 on a loss.

Imagine a scenario where you play a game and the opponent plays poorly and you win; you then try and repeat the same thing again, this time the opponent has learnt from their mistakes and beats you. You'll keep playing the same losing move significantly more times because it worked that one time.

I don't know why everyone wants to second-guess the first chapter of the standard textbook in this space with what seems like no experience even thinking about this topic...

When you lose the value of v' change and so change the value of v.

Short and mathematical: "Algorithms for Reinforcement Learning". PDF available: https://sites.ualberta.ca/~szepesva/RLBook.html

Thanks, the online pdf seems to be very good.

I did my thesis about reinforced learning. Unfortunately didn't work with that afterwards, but I really think it's one of the most interesting approaches to machine learning for real world application.

What was your thesis? And what are some areas do you see RL being applied (besides the somewhat contrived game solvers we've been seeing)?

Can someone please provide a mirror? Both links don't work anymore! Thanks a lot guys

Is the link still available? No seeders anymore ?

Don't get anything from it.


Try now.

There is also information about the book on the author's page about it on his website:


How is it different from the first edition?

There's a "Preface to the Second Edition" near the front, which has a summary of changes. Main points are: 1) notation was overhauled, 2) Chapters 2-8 were reworked to only use tabular methods, with function approximation introduced later; 3) the function approximation coverage is then greatly expanded in the second section of the book (Chs. 9-13); and 4) new chapters 14-15 on connections between RL and psychology and neuroscience.

The scope is generally about the same though, perhaps because it's intended to be used as a single-semester textbook, so there isn't a big expansion into areas of RL other than those covered in the first edition (e.g. POMDPs are only briefly mentioned).

Does any one has a mirror?

Hey, I hope this doesn't come across as rude, but it seems like you're not a native English writer. The correct grammar for your question would be:

Does anyone have a mirror?

edit: Most people like when natives correct their English.

OP is a cat. The grammar is correct.

Wait, shouldn't that be "Can I haz mirror?"?

Will RL take over Deep Convolutional Networks as having the best results?!

I think they are parallel and can be used together, like Deep Reinforcement Learning.

I think they are parallel and can be used together



That's a sequential use. They got the initial strategy with DL, and made it stronger with MCTS

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