# Monte Carlo AI in 21 game

I am very interested in the Monte Carlo AI.

I tried my best, still, this AI plays very badly.

This code "works" in the meaning that it does not crash, but the quality of play is extremely low.

Have I completly misunderstood the Monte Carlo AI or is there just a nasty bug in my code preventing it from playing correctly?

Can you understand every detail of code?

Creating an AI is quite complex a task so I would like to have the better style possible in order to keep the code understandable by everyone.

"""
TITLE: Monte Carlo AI that plays the 21 game
AUTHOR: Caridorc Tergilti
CURRENT_STATE: The AI is currently very weak, I have no clue why.

General explanation:
This game is very,very easy, an AI can be disigned in
much simpler ways than this.

I designed a Monte Carlo AI because I want to learn about Monte Carlo AI-s
(You can find out more about Monte Carlo here:
https://en.wikipedia.org/wiki/Monte_Carlo_method)
and I want to discover if such an AI can actually be powerful in a game.

(Yes, there are many articles and papers on the internet that say that
Monte Carlo AI works but I want to prove it myself).

This programme is also Creative Commons so I hope that many people will enjoy
reading this programme to understand Monte Carlo AI better.

I will now explain my understanding of Monte Carlo AI.

1) The computer, knowing the rules, must decide what move is better.
2) FOREACH move the computer is going to simulate the move.
3) It will than play a big number of random games starting from the position after he made
the move.
4) Each move will be awarded a score equals to: wins / total_games.
5) The move with the highest score will be played.
"""

import random

WELCOME = """
21 game.
The total starts at 0.
Each player can chose 1, 2, or 3,
the number is than added to the total.
If when a player adds his number,
he makes the total equals or more than 21, he loses.
"""
DEPTH = 1000

def play_random_game(state):
"""
This function plays a random game starting
from a state.
The opponent moves first.
Returns 1 if you win,
0 if you lose
"""
if state >= 21:
return 0 # You previously played a move that made you lose
while 1:
state += random.randint(0,3) # Opponent
if state >= 21:
return 1   # YOU WIN
state += random.randint(0,3)
if state >= 21:
return 0   # YOU LOSE

def play_n_games(state,number):
"""
Plays a certain number of random games,
all starting from the state.
Return the wins/total ratio.
"""
outcomes = []
for _ in range(number):
outcomes.append(play_random_game(state))
return sum(outcomes) / len(outcomes)

def AI(total):
"""
This artificial intelligence uses Monte Carlo
to make the best move.
"""
list_of_outcomes = []
possible_moves = [1,2,3]
for move in possible_moves:
list_of_outcomes.append(play_n_games(total,DEPTH))

# Chose the move with the better score
if max(list_of_outcomes) == list_of_outcomes:
return 1
elif max(list_of_outcomes) == list_of_outcomes:
return 2
elif max(list_of_outcomes) == list_of_outcomes:
return 3

def interface():
"""
Allows the user to play the game
against the AI.
"""
total = 0
print(WELCOME)
while 1:
user_number = int(input("Enter your number: "))
assert (user_number in [1,2,3]),"Each player can chose 1, 2, or 3"
total += user_number
print("The total is " + str(total))
print()
if total >= 21:
print("You lost")
break
total += AI(total)
print("After the AI added a number")
print("The total is " + str(total))
print()
if total >= 21:
print("You won")
break

if __name__ == "__main__":
interface()

• For those who are not aware of it: The ultimate strategy to this game is described here Oct 24 '14 at 9:25
• @SimonAndréForsberg to play the best possible you have to always make the number dividble by 4. Ex (2 -> 4) (13 -> 16). Oct 24 '14 at 13:16
• Yes, I know, which is exactly what the wikipedia article says. Perhaps I should have linked directly to the 21 game part though. Oct 24 '14 at 13:25

There's also a simple bug: you generate random moves by randint(0,3). This should of course be randint(1,3) because 0 is not a valid move.