Following a visit to Las Vegas a few months ago, I developed an interest in blackjack. According to Wikipedia, the house edge for a casino is typically around 1%. Rather than performing a statistical analysis, I decided to write a blackjack-playing bot and measure its losses.
This seemed like the perfect opportunity to get my feet wet with Haskell. A card game required minimal third party libraries and seemed well-suited to playing with Haskell’s type system.
The Haskell learning curve
Haskell is not an easy language to get started in. There are excellent online books to get you started (1, 2), and #haskell on Freenode were very helpful. However, the learning curve is difficult at the start.
Every Haskell program requires the programmer to have a moderate
understanding of at least the type system and the IO monad. There is a
substantial amount of syntax in just this. For example, you will want
to learn the
(though some consider it harmful)
which includes the unfortunately named
greetingForUser is a value that, when evaluated, takes a line of
input from stdin and returns a string containing that name wrapped in
a friendly message.
So, what’s going here?
getLine has type
IO String, a
string which is marked as having come from the outside, impure
world. You can evaluate it twice and get different
do notation allows us to use the string as a simple
String, enabling us to keep
IO actions out of the third line.
return then wraps
Got that? Understanding
IO is a conceptual hurdle you must overcome
before you can start writing useful code.
There are a number of syntax gotchas which must be learned to write
code effectively. For example, getting the type of array access can
produce parse errors (
:type !! is incorrect,
:type (!!) should be
used). To make matters worse, syntax between the REPL and source code
files is slightly different. The excellent
Hoogle search engine goes a long way
to fix this. For example, you can search for functions like
are not Google-friendly.
Haskell offers very powerful, very high-level tools for coding. Once you’re comfortable with the normal syntax, you can even write macros. With such high-level tools, it’s often possible to greatly abstract concepts.
For example, every value in Haskell has a type. Every type has a kind, which is essentially a meta type. GHC (the most commonly used interpreter/compiler) even supports a number of extensions to this meta type system.
So the Haskell programmer has to really think about their code, up front. This is hard for a beginner.
For the persistent learner, Haskell helps you think about the effects and assumptions of code in a rigorous way.
For instance, using only pure functions for the core of a program does produce more predictable, debug-friendly, testing friendly code. This is a perspective that has improved my code in impure languages.
Here’s an example from the bot. I originally dealt cards by randomly chosing cards one-by-one from the deck:
This forced more of my functions to be impure. Any function whose output is dependent on a random number generator is a pain to test. With Haskell exposing this in the type system, this defect stares you in the face. I later refactored this into a superior function that reorders the whole deck in one go:
The combination of Haskell’s type system and syntax forces you to think about every situation that your function needs to deal with. Once the code is written, this is wonderful. Code resulting from this process is generally robust and reliable. However, I found writing the code to be less satisfying, as it’s hard to write a function piece-by-piece.
In any language, it’s well worth using lint tools to examine code to find potential errors or issues. This has saved me countless hours of debugging. In Python, I use the excellent Pyflakes to catch mistakes before I’ve even run the code.
It’s shocking how many of errors simply cannot occur in Haskell
code. Haskell’s syntax and type system not only eliminated bugs that I
could catch with lint tools, they prevent bugs that I can only detect
at runtime in other languages. For example, the type system forces you
to label any value that may be null with
Maybe, eliminating null
pointer exceptions entirely.
Originally, I would run the simulation with 10,000 hands, since it showed less variance in losses. I read later that a player typically plays 80 hands in an hour, so I tried simulating an hour’s play. 80 hands of blackjack produces a much larger range of outcomes, giving the game a completely different feel.
I had also intended to implement a perfect strategy inside my simulator. It became clear that a relatively simple strategy did not produce losses significantly less than a perfect strategy. I found that the opposite was generally true for house rules though. Small changes in house rules can substantially different losses.
Would I choose to play Blackjack if I visit Las Vegas again? I might have a clear idea of strategy now, but having lost over $100,000 in virtual money so far, it’s probably unwise.
Getting the code
My blackjack code is available on GitHub. The player’s tactics are not optimal, but losses are around 2%.