*This post is part of a series on Mohammad Anwar’s excellent Weekly Challenge, where hackers submit solutions in Perl, Raku, or any other language, to two different challenges every week. (It’s a lot of fun, if you’re into that sort of thing.)*

Task #2 this week can be as simple or as difficult as you make it. Mohammad’s description:

Suppose there are following coins arranged on a table in a line in random order.

£1, 50p, 1p, 10p, 5p, 20p, £2, 2p

Suppose you are playing against the computer. Player can only pick one coin at a time from either ends. Find out the lucky winner, who has the larger amounts in total?

## Analysis

The problem is quite specific, in that we are only given one possible input list, and it is set up as a human v. computer contest, with no other challenge as to how smart (or stupid) the computer is. In fact, this *particular* problem can be reduced to: “whomever gets the £2 piece wins,” as all the other coins add to 188p.

If you go first, picking £1 will force the opponent to pick 50p or 2p. Keep going until your opponent picks the 2p piece (or the 20p piece), then you get the £2 piece and win.

If the opponent goes first, the same thing applies. Keep stalling until they pick the 20p or 2p piece, then pick the £2 piece and win. Unless of course your opponent is using the same strategy, in which case, they’ve stalled *you, *and you lose.

So, this isn’t terribly interesting, but I’ll implement it in Raku anyway. Stick around for the Perl version if you yearn for a more general version, though!

## Raku (Simple)

So, for a baseline, here is my Raku solution, that keeps it simple and does exactly what is asked:

```
#| A more literal reading of the problem, with a simple greedy CPU
sub MAIN( Bool :$me-first ) {
my @coins = @*ARGV ?? @*ARGV !! <100 50 1 10 5 20 200 2>;
my $my-score = 0;
my $cpu-score= 0;
my $my-turn = $me-first;
while (@coins) {
say @coins;
if ($my-turn) {
my $lr;
repeat { $lr = prompt "Move [lr]? " } until 'lr'.index($lr) ≥ 0;
$my-score += $lr eq 'l' ?? @coins.shift !! @coins.pop;
} else {
$cpu-score += @coins[0] > @coins[*-1] ?? @coins.shift !! @coins.pop;
}
$my-turn = !$my-turn;
}
say $my-score > $cpu-score ?? "You win" !! "CPU wins";
say "Your score: $my-score. CPU score: $cpu-score";
}
```

Even then, I still went a bit beyond the spec, by allowing a user-specified list of `@coins`

.

The computer uses a greedy algorithm, which performs reasonably well (we’ll get to that in a bit), but isn’t optimal. That “algorithm” is just one line in this case:

```
$cpu-score += @coins[0] > @coins[*-1] ?? @coins.shift !! @coins.pop;
```

To do better than greedy, we would need only replace that line with code (or a function call) to make a smarter decision. Without looking ahead, greedy is obviously the best we can do.

## Going (a lot) further (Perl)

While I went for a barebones solution with Raku, I went completely overboard with Perl. I wanted to go beyond the task description and write a general solution for any number of “coins,” of any value.

I have my reasons for this: since I’ll be reviewing everyone else’s solutions this week, I wanted a platform I could build on to make some comparative statements about any solutions that tried to make a “smart” computer. Plus it was fun and I got a bit carried away. OK, it was mostly that it was fun and I got a bit carried away. What can I say. I make my own amusement.

I made a complete application with arguments and Pod. Here is the help, as generated with `pod2github`

:

# NAME

ch-2.pl – Lucky Winner Simulator 9000

# SYNOPSIS

```
ch-2.pl [options] [algorithm1 algorithm2 ...]
ch-2.pl --human=<cpu_algorithm>
ch-2.pl --help
```

# OPTIONS

```
--count=<iter> Play <iter> games Default: 1000
--coins=<N> Every game uses <N> coins Default: 8
--maxcoin=<N> Maximum coin value Default: 200
--help Full help page
--human=<cpu_alg> Human vs CPU, CPU uses <cpu_alg>
--seed=<N> Use specific random number seed (integer)
--verbose Enable extra output
--noverbose Disable extra output
```

# ALGORITHMS

`human` | Human input. Only available with `--human` option. |

`bozo` | Real stupid algorithm; chooses left or right randomly. |

`worst` | Somehow even stupider. Always picks lowest option. |

`greedy` | Greedy algorithm. Always picks highest option, but doesn’t look ahead. |

`ahead1` `ahead3` `ahead5` | Looks ahead 1, 3, or 5 turns, and picks the option that maximizes (my_score – their_score) |

The script has two basic modes: `--human`

mode, and CPU-only mode. With `--human=greedy`

, for example, the human player will compete against a greedy CPU. You will be prompted for a left/right choice. What I think is a lot more interesting, though, is the CPU-only mode, which pits the CPU against itself in a round-robin battle between every CPU algorithm, and tallies the results to see which one emerges victorious overall. Here is a sample run with the default settings:

```
Player0 v Player1 | Wins0 - Wins1
--------------------------------------------------
ahead1 v ahead3 (W) | 920 - 1080
ahead1 v ahead5 (W) | 924 - 1076
(W) ahead1 v bozo | 1796 - 204
(W) ahead1 v greedy | 1134 - 866
(W) ahead1 v worst | 1999 - 1
ahead3 v ahead5 (W) | 987 - 1013
(W) ahead3 v bozo | 1771 - 229
(W) ahead3 v greedy | 1208 - 792
(W) ahead3 v worst | 1991 - 9
(W) ahead5 v bozo | 1744 - 256
(W) ahead5 v greedy | 1246 - 754
(W) ahead5 v worst | 1987 - 13
bozo v greedy (W) | 263 - 1737
(W) bozo v worst | 1764 - 236
(W) greedy v worst | 2000 - 0
Leaderboard:
ahead5: 7066 wins
ahead3: 7037 wins
ahead1: 6773 wins
greedy: 6149 wins
bozo: 2716 wins
worst: 259 wins
```

The first block shows individual match-ups. For each, `--count`

(default: 1000) games are played where player 0 goes first, then another `--count`

games are played where player 1 goes first, so it’s fair. The wins for each are shown on the right (so, in the `ahead1 v ahead3`

matchup, `ahead1`

won 917 games, and `ahead3`

won 1083. I put a `(W)`

beside the one with the most wins, but this doesn’t mean anything to the overall `Leaderboard`

stats.

The `Leaderboad:`

section is a simple tally of wins for each algorithm, regardless of opponent. Notice how `worst`

managed to pick up 259 total wins, despite always picking the smallest of the two coins available!

We can see pretty clearly that while `greedy`

has a respectable showing, looking farther ahead has an advantage. Sometimes it’s better to pick a lower valued coin to force your opponent into giving you an even bigger coin, after all. Looking ahead definitely seems to have diminishing returns after a few turns.

I could have done much, much more with this, but it’s already over 250 lines. The full code is available here. I’ll show you some interesting snippets:

### Algorithms

The “easy” algorithms are all one-liners in a dispatch table. Given a list of coins, each `sub`

returns 0 for left, and 1 for right, and is called for every turn:

```
sub get_algorithms {
(
bozo => sub { rand > 0.5 },
worst => sub { $_[0] > $_[-1] },
greedy => sub { $_[0] < $_[-1] },
ahead1 => \&ahead1,
ahead3 => ahead(3),
ahead5 => ahead(5),
);
}
```

Not shown here is the `human`

sub; that one simply prompts for a left/right choice.

The `ahead`

sub is the complex one. It can handle any depth, and so it returns a closure around the specified number of moves (`$n`

), for the dispatch table:

```
# Look ahead n moves
sub ahead {
my $n = shift;
sub {
my $ahead = sub {
my ($depth, $us, $lr, @coins) = @_;
my $val = $us * ($lr == LEFT ? shift @coins : pop @coins);
return $val if !$depth or @coins == 0;
my $f = $us == 1 ? \&min : \&max;
$val + $f->(
map { __SUB__->($depth-1, -$us, $_, @coins) } LEFT, RIGHT
);
};
$ahead->($n, 1, LEFT, @_) >
$ahead->($n, 1, RIGHT, @_) ? LEFT : RIGHT;
};
}
```

This is a straightforward combinatorial game theory approach. The `$ahead`

sub seeks to maximize the current player’s score while minimizing the other player’s maximum score. It grows exponentially, so it’s best to keep `$n`

small (or not call it often).

The rest of the code is boring tally, display, and Pod (documentation), but you can view it here.