Clone this project and initialize the mm submodule
$ git clone git@github.com:psaris/wordle.git
$ git submodule update --init --recursiveStart q with the following command to see how an optimal initial word
is chosen as well as an example of playing the game interactively.
Running with multiple secondary threads -s 4 allows the
computationally-heavy search for optimal guesses to run in parallel.
$ q play.q -s 4Similar to Mastermind, the goal of Wordle is to discover the hidden word (or 'code' in Mastermind parlance) in the least number of guesses. At each step of Mastermind you are told how many pegs are placed in the correct space and how many are the correct color, but placed in the wrong space. Wordle adds a slight twist to the scoring function. Instead, the game tells you the actual letters that are in the correct place and the actual letters that are in the wrong place. This reveals much more information and drastically simplifies the search process.
Another difference between Mastermind and Wordle is that while every
possible Mastermind guess can be the actual solution, Wordle allows
you to guess words that are not in the solution space. How do I know?
The game solutions and all valid guesses are loaded by the web page
and are available by inspecting the page source and clicking on the
main.7785bdf7.js link (where the 7785bdf7 hash may change in
the future.
Every step of the game solves the same problem: how can I reveal as
much information about the solution as possible. Intuitively,
choosing a word with rarely used letters is likely to reveal very
little information. But what combination of letters reveals the most
information? This fundamentally comes down to the distribution of
possible response. If we represent the three responses gray, yellow
and green as " YG", we can intuit that there will be approximately 243
(3 xexp 5) possible responses. In practice, there are only 238
responses because it is not possible to have all letters correct
except for one, which is the correct letter but in the wrong location.
Concretely, the following responses are not possible:
"YGGGG"
"GYGGG"
"GGYGG"
"GGGYG"
"GGGGY"A good guess would have all remaining codes distributed as evenly as
possible among the possible responses. If we could achieve this
perfect distribution, the first guess would narrow the total universe
of remaining codes from 2309 to 10 (2309 % 238) on the first
guess. How can we find this magic word? There are many algorithms to
chose from. One way is to choose the word that minimizes the maximum
size of each possible response set (.mm.minimax). This method was
introduced by Donald Knuth to solve the Mastermind game. Another way
is to choose the word that minimizes the average response set size
(.mm.irving). There is, in fact, an information-theoretic
calculation that measures how unevenly (or randomly) values are
distributed: entropy. When all responses to our guess are the same,
the entropy is 0. When all responses are evenly distributed, entropy
is maximized. So which word maximizes the response entropy?
First we load the mm and wordle libraries mm/mm.q and wordle.q and
replace the .mm.score function with a vectorized version of the
wordle scoring function .wordle.scr.
q)\l mm/mm.q
q)\l wordle.q
.mm.score:.mm.veca .wordle.scrThen we load all possible solutions from answers.txt and valid
guesses from guesses.txt. And finally, we call the .mm.freqt
function to generate the frequency table of all possible first
guesses.
q)C:`u#asc upper read0 `:answers.txt
q)G:`u#asc C,upper read0 `:guesses.txt
q)show T:.mm.freqt[G;C]
score | AAHED AALII AARGH AARTI ABACA ABACI ABACK ABACS ABAFT ABAKA ABAMP ABAND ABASE ABASH ABAS..
-------| ----------------------------------------------------------------------------------------..
" "| 448 667 587 378 993 624 925 668 772 1134 896 753 422 702 805 ..
" G"| 40 3 64 4 26 3 24 15 123 30 27 49 151 66 34 ..
" Y"| 60 100 229 366 44 310 214 164 124 297 102 31 ..
" G "| 175 56 34 40 92 56 62 78 15 30 19 87 45 72 92 ..
" GG"| 21 7 2 30 5 2 11 12 33 10 3 ..
" GY"| 29 10 2 26 36 14 2 4 6 17 13 ..
" Y "| 303 308 76 114 159 107 147 124 82 80 108 184 158 235 237 ..
" YG"| 10 2 4 1 6 7 13 5 49 5 23 ..
" YY"| 63 13 94 51 6 28 18 15 24 72 39 19 ..
" G "| 39 73 70 187 159 159 104 158 213 195 180 80 117 124 ..
" G G"| 13 6 3 15 2 28 5 4 9 38 11 18 ..
" G Y"| 12 12 25 13 81 44 27 17 31 21 7 ..
" GG "| 3 8 8 6 28 28 15 22 3 11 6 25 23 19 32 ..
" GGG"| 13 4 7 3 6 11 1 ..
" GGY"| 4 6 1 1 4 3 ..
..We can now demonstrate which words have the maximum entropy, thus revealing the most information and maximizing our chance of shrinking the set of remaining valid solutions.
q)5#desc .mm.entropy each flip value T
SOARE| 4.079312
ROATE| 4.079072
RAISE| 4.074529
REAST| 4.067206
RAILE| 4.065415This is the optimal starting word for the maximum entropy algorithm. As we will see below, each algorithm has its own best starting word.
One game per day can be played on the official Wordle site. But with our implementation, we can play as many times as we want.
After loading the library and word list, we can define our first guess
g, algorithm a and let the algorithm play a random game.
q)\l mm/mm.q
q)\l wordle.q
q)C:asc upper read0 `:answers.txt
q)G:asc C,upper read0 `:guesses.txt
q)g:"SOARE"
q)a:.mm.onestep `.mm.maxent
q).mm.summary each .mm.game[a;G;C;g] rand C
n guess score
--------------------
2309 "SOARE" " G G"
28 "GLITZ" " "
8 "HEAVE" " GG G"
1 "PEACE" "GGGGG"Alternatively, we can change the algorithm to prompt us for our own guess (while hinting at the algorithm's suggestion).
q)a:.mm.stdin .mm.onestep `.mm.maxent
q).mm.summary each .mm.game[a;G;C;g] rand C
n guess score
--------------------
2309 "SOARE" " YY "
guess (HINT GLITZ): GLITZ
GLITZ
n guess score
------------------
28 "GLITZ" " "
guess (HINT HEAVE): HEAVE
HEAVE
n guess score
-----------------
8 "HEAVE" " GG G"
guess (HINT PEACE): PEACE
PEACE
n guess score
--------------------
2309 "SOARE" " G G"
28 "GLITZ" " "
8 "HEAVE" " GG G"
1 "PEACE" "GGGGG"There are many algorithms to finding the best word at each step. The
following are included in the mm library.
.mm.minimax.mm.irving.mm.maxent.mm.maxgini.mm.maxparts
Each algorithm has different performance characteristics. In order to measure the distribution and average number of guess required to win, we will apply the algorithm across all possible codes. To make this process more efficient, we first cache the scoring function by converting it into a nested dictionary.
.mm.score:.mm.scr/:[;C!C] peach G!GWe can now use the .mm.best function to scan all possible options
for the best first algorithm-dependent code. The first code for
.mm.minimax, for example, is "ARISE".
q).mm.best[`.mm.minimax;G;C]
"ARISE"Running through all games,.mm.mimimax can get the correct answer in
one shot (because it starts with a word from the code list). The
downside is that it may take up to 6 attempts -- with an average of
3.575574 attempts.
q)show h:.mm.hist (count .mm.game[.mm.onestep[`.mm.minimax];G;C;"ARISE"]::) peach C
1| 1
2| 53
3| 982
4| 1164
5| 107
6| 2
q)value[h] wavg key h
3.575574The .mm.irving (minimum expected size) algorithm starts with a
non-viable code, but guarantees a solution in 5 attempts and an even
better average of 3.48246 attempts.
q)show h:.mm.hist (count .mm.game[.mm.onestep[`.mm.irving];G;C;"ROATE"]::) peach C
2| 55
3| 1124
4| 1091
5| 39
q)value[h] wavg key h
3.48246The information theoretic maximum entropy .mm.maxent can't get the
answer in one attempt and has a worst-case scenario of 6 attempts. But
it wins with an average 3.467302 attempts -- beating the previous two
cases.
q)show h:.mm.hist (count .mm.game[.mm.onestep[`.mm.maxent];G;C;"SOARE"]::) peach C
2| 45
3| 1206
4| 993
5| 64
6| 1
q)value[h] wavg key h
3.467302Finally, we try the .mm.maxparts algorithm and observe that it has the
best average seen so far: 3.433088.
q)show h:.mm.hist (count .mm.game[.mm.onestep[`.mm.maxparts];G;C;"TRACE"]::) peach C
1| 1
2| 75
3| 1228
4| 935
5| 68
6| 2
q)value[h] wavg key h
3.433088Reviewing the interactive play from above shows the first three guesses have very few letters in common. This very efficiently narrowed the remaining options such that the fourth guess could only have been a single word. First-time players of Wordle, however, typically continue to use letters revealed to be correct. This is intuitive but sub-optimal -- at least in the first few guesses.
Wordle allows users to enable 'hard mode', which forces players to use this sub-optimal approach of using the letters that have been revealed to be correct. Specifically, any letter marked green must be used again in exactly the same place and any letter marked yellow must be used again but not necessarily in the same place. Hard mode limits the allowed guesses, thus slowing the information-gathering process and increasing the average number of guesses required to find the code.
To enable 'hard mode', we can redefine the .mm.filtG function with
the Wordle variant .wordle.filtG.
.mm.filtG:.wordle.filtGReplaying the above game, we can see that the code was found in three guesses instead of four. Wonderful!
q).mm.summary each .mm.game[a;G;C;g] "PEACE"
n guess score
--------------------
2309 "SOARE" " G G"
28 "PLAGE" "G G G"
1 "PEACE" "GGGGG"But running the game across all codes reveals how the extra constraint adds to the difficulty of the game. In one case, the algorithm can't even win in 6 guesses -- the maximum allowed on the Wordle site -- and the average number of guesses per game has jumped to 4.614985.
q)show h:.mm.hist (count .mm.game[.mm.onestep[`.mm.maxent];G;C;"SOARE"]::) peach C
2| 3
3| 86
4| 822
5| 1285
6| 112
7| 1
q)value[h] wavg key h
4.614985