Thanks for the answer guys!

Really cool job man.

I agree with your comments -- could you email me a copy of the code, and I'll see if I can find the difference?

Yea, minotaurs and Sevendeuceo pretty much nailed it, but I'll put things in my own words --

The series is meant for people with no prior programming experience, just an interest in learning. That said, it doesn't attempt to be an "intro to programming" in general -- I focus on the concepts that are necessary to solve our problem. As far as book-related prerequisites, it really just uses the material in chapters 1 and 2 from the first book.

So, I think it'd be a great fit for you! But I might be biased. We made 4 of the videos (the first three and one from the middle, almost 2 hrs of footage) available for free -- I imagine that watching those would be the best way to see if the pack is right for you.

Hey so i almost finished with video series with Sevendeuceo's help but in the end i had this thing...
So when i did
i got exactly same EV's for every iteration and also ranges in the end looked the same as in video. I was very happy if i must say
Than next step was to create the same thing for our big decision tree which also looked the same as Wills in the picture. Starting ranges also were the same but then when i did 300 iterations EV's was a bit different

Mine
Will's
Pictures also was different.
This is my 1: BB check

This is Wills

So if i get resaults from minraise shove game exactly the same it must be the case that all the functions work fine but my big decision tree was different at some point right?

same problem from my question minotaurs, though interestingly your numbers are different than both mine and the video's...I've forwarded my stuff to Will so he can try to figure out what's going on.

Yea interesting. Sevendeuceo has the same problem as well. Maybe you can let us know as well when Will tells you whats going on there?

Hey Will whats the biggest game you tryed to make with this program. I saw that you wrote on husng.com that the biggest issue was always the RAM and that every board takes 57MB and every action takes 3 * 21 kB. This memory takes place into RAM and only after that those board files are stored in the HDD? Sry i just dont realy know how it works.
Also whats your CPU and how much time it took for that tree?
And also if i get computer with 4 cores does IPython is using them all when calculating "stuff" in this program or just 1?

Ty im very interested in these questions

I also bought this series.

Havent had time to do them all yet, so far im only at video 5.

I really enjoy it. Although the sound quality could have been a litle better but that is a minor isue.

I have no prior programing expirience, but I was always a bit facinated about computer programming it so I thought this was a good chance for me to get into programing while I can also learn something about poker.

I will say you dont need any programing expirience to follow the series, but if you are interested there are a ton off free videos and other material on the internet. about python.

I started to do and online course in computer science MITx6,0 on edx.org

https://courses.edx.org/courses/MITx...0.1_3x.2T2014/

Its free and they use python 2.7 in all the examples. Its the same version of python that commes with the anaconda pack that will uses in the video's ( although will uses Ipython wich is slightly diferent) So for anyone interested to learn a litle more about python i will highly recommend it as it will also make it more easy to understand what will is doing in the video's.

I will almost surely buy this pack.

Another question: Is it possible to find near-optimal solutions for "real" poker situations using these methods or are they restricted to push/fold situations? Can i figure out 100bb+ stuff? (not find equilibrium of course, but find something that comes reasonably close or at least eliminates things that are definitely bad strategy)

I like the idea of this as a means to figure out what exactly is going on. To take a deeper look into how poker works. But what can i expect compared to softwares like cardrunnersEV?

The average EVs that I got fo the first few iterations of doFP() with the big tree are thus:

Iter: 1 SB average EV: 19.9656130555 BB average EV: 22.7199843776
Iter: 2 SB average EV: 19.7179824410 BB average EV: 22.2639922618
Iter: 3 SB average EV: 19.6475879577 BB average EV: 21.9607905740
Iter: 4 SB average EV: 19.6216371940 BB average EV: 21.7087385284
Iter: 5 SB average EV: 19.6237905954 BB average EV: 21.5416749776

Similar issue as others reported. It would be nice if at least two of us got the same results!

(Does it look like what you got, stevepa?)

By studying this video pack will I be able to solve the game for some MTT HU situations for about 20bb? For example villain range is known and hero is closing action on the BB? Or blind vs blind situations where SB plays OOP?

In the case where villain's range is known, then 'solving' the game isn't even required. You just find the strategy that maximizes EV against Villain's range. This is covered early in the video series. But you further can find the optimal strategy in the GTO sense (unexploitable), which would maximize your EV assuming villain also plays optimally against whatever strategy you choose.

In the video series, the min-raise/shove game is solved for some given min-raise size. You can, of course, chose other sizes. This is most relevant to 20bb HU spots in cash games. However, in multi-table tournaments, your goal is to maximize you share in the prize-pool, and not just maximize EV for each individual hands. So, you will need to adapt what is learned to such situations. There is some discussion of this in Tipton's book, vol.2.

Sure, there are many applications to more complex situations. Where simplifications come in is in defining some sub-game tree, with some assumptions about bet sizes, and assuming starting ranges for both players in the spot you wish to analyse. Once this is defined, and assuming the tree isn't too complex, you can solve it exactly and find the Nash equilibrium (or get close to it after many iterations of Will's 'fictitious play' algorithm). Because of the assumptions on bet sizes and the simplification of the tree structure, this can't guarantee that the play truly is GTO in that spot -- though it is, conditionally on those constraints. But it may well be even more instructive and fruitful than possession of the true solution would be. That's because you are forced to connect the analysis to real game situations and decision points as you encounter them.

It's also a great complement to cardrunnersEV.

sevendeuce - very similar to mine but not identical. Very odd.

klondi - cool site, but link doesn't seem to work? edit: found it

This course looks quite interesting. Unfortunately registration is closed.

I did the python course on codeacademy before this pack.
+- 13 hours estimated by them.
Extremely friendly to people that never ever coded before.
And if you know 1 or more other languages; you can go through it extremely fast.

I also have same issues as others:
here are mine numbers for first 5 iterations
Seems that my numbers are exactly the same as Sevendeuceo
Let's see what Yagh has to say about the review of stevepa; I think that alot of us have the same or simular problem in their code.

What I am looking for is to solve min raise/3bet-small/4bet-shove, min raise/3bet-shove, min raise/flat call game with some postflop simplifications where inital min-raise range is fixed.
Is it possible or is to ambitious?

That seems quite feasible.

Having trouble getting pydot to work on windows 8:

1st gif shows all the steps described in the video, completed (Video 11)
http://gyazo.com/bcff1511a47b5441c6ef9340feee6a11
http://gyazo.com/56683756f0c25e1b3d13372f351cc2b6
http://gyazo.com/d81187db628e7b23a050688156092b42

~5:00 shows how to change the path variable, but the address he copies from explorer and what is in the path variable (which he doesn't change)
one is the install folder, and the other is "installfolder\bin\"
For good measure, I added both to my PATH variable, both for user and system.
Still not working.

Help?
Thanks!

Duplicate message. Can't delete, for some reason.

Only the ...\bin\ path is required. This was a mistake in Will's video but his PATH variable already was correctly set. You did close your notebook and restart iPyton? What error message do you get?

http://gyazo.com/4d89b012a3f45a0e80d2f943c0c31f36

Tried it again with the bin path, as demonstrated here. Yes, I've closed and restarted every time I've tried to make a change.

I've tried installing it into different foldings and changing the corresponding \bin\ location, but no luck.
I've also tried extracting the zip directly, which is the result seen in the above .gif.

The error message is always the same.

n [11]:

import pydot

---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-11-8189bd158075> in <module>()
----> 1 import pydot

ImportError: No module named pydot

I am flaggerbasted. Consider also asking the question in some iPyton forum.

ZhengHe,

Your Anaconda command window states that "pydot" was installed. That should be pydot2.

Might have you mistakenly typed "pip install pydot" instead of
"pip install pydot2" ?