Javascript

So my friend tells me my solution is close to Dijkstra but honestly I just tried what made sense until it worked. I originally wanted to just bruteforce it and get every single possible path explored but uh… Yeah that wasn’t gonna work, I terminated that one after 1B operations.

I created a class to store the state of the current path being explored, and basically just clone it, sending it in each direction (forward, 90 degrees, -90 degrees), then queue it up if it didn’t fail. Using a priority queue (array based) to store them, I inverted it for the second answer to reduce the memory footprint (though ultimately once I fixed the issue with the algorithm, which turned out to just be a less than or equal to that should have been a less than, I didn’t really need this).

Part two “only” took 45 seconds to run on my Thinkpad P14 Gen1.

My code was too powerful for Lemmy (or verbose): https://gist.github.com/zikeji/9a3df795c815456ee51dacceb3701824

C

Yay more grids! Seemed like prime Dijkstra or A* material but I went with an iterative approach instead!

I keep an array cost[y][x][dir], which is seeded at 1 for the starting location and direction. Then I keep going over the array, seeing if any valid move (step or turn) would yield to a lower best-known-cost for this state. It ends when a pass does not yield changes.

This leaves us with the best-known-costs for every reachable state in the array, including the end cell (bit we have to take the min() of the four directions).

Part 2 was interesting: I just happend to have written a dead end pruning function for part 1 and part 2 is, really, dead-end pruning for the cost map: remove any suboptimal step, keep doing so, and you end up with only the optimal steps. ‘Suboptimal’ here is a move that yields a higher total cost than the best-known-cost for that state.

It’s fast enough too on my 2015 i5:

day16  0:00.05  1656 Kb  0+242 faults

#include "common.h"

#define GZ 145

enum {NN, EE, SS, WW};

static const int dx[]={0,1,0,-1}, dy[]={-1,0,1,0};

static char g[GZ][GZ];		/* with 1 tile border */
static int cost[GZ][GZ][4];	/* per direction, starts at 1, 0=no info */

static int traversible(char c) { return c=='.' || c=='S' || c=='E'; }

static int
minat(int x, int y)
{
	int acc=0, d;

	for (d=0; d<4; d++)
		if (cost[y][x][d] && (!acc || cost[y][x][d] < acc))
			acc = cost[y][x][d];

	return acc;
}


static int
count_exits(int x, int y)
{
	int acc=0, i;

	assert(x>0); assert(x<GZ-1);
	assert(y>0); assert(y<GZ-1);

	for (i=0; i<4; i++)
		acc += traversible(g[y+dy[i]][x+dx[i]]);
	
	return acc;
}

/* remove all dead ends */
static void
prune_dead(void)
{
	int dirty=1, x,y;

	while (dirty) {
		dirty = 0;

		for (y=1; y<GZ-1; y++)
		for (x=1; x<GZ-1; x++)
			if (g[y][x]=='.' && count_exits(x,y) < 2)
				{ dirty = 1; g[y][x] = '#'; }
	}
}

/* remove all dead ends from cost[], leaves only optimal paths */
static void
prune_subopt(void)
{
	int dirty=1, x,y,d;

	while (dirty) {
		dirty = 0;

		for (y=1; y<GZ-1; y++)
		for (x=1; x<GZ-1; x++)
		for (d=0; d<4; d++) {
			if (!cost[y][x][d])
				continue;

			if (g[y][x]=='E') {
				if (cost[y][x][d] != minat(x,y))
					{ dirty = 1; cost[y][x][d] = 0; }
				continue;
			}

			if (cost[y][x][d]+1 > cost[y+dy[d]][x+dx[d]][d] &&
			    cost[y][x][d]+1000 > cost[y][x][(d+1)%4] &&
			    cost[y][x][d]+1000 > cost[y][x][(d+3)%4])
				{ dirty = 1; cost[y][x][d] = 0; }
		}
	}
}

static void
propagate_costs(void)
{
	int dirty=1, cost1, x,y,d;

	while (dirty) {
		dirty = 0;

		for (y=1; y<GZ-1; y++)
		for (x=1; x<GZ-1; x++)
		for (d=0; d<4; d++) {
			if (!traversible(g[y][x]))
				continue;

			/* from back */
			if ((cost1 = cost[y-dy[d]][x-dx[d]][d]) &&
			    (cost1+1 < cost[y][x][d] || !cost[y][x][d]))
				{ dirty = 1; cost[y][x][d] = cost1+1; }

			/* from right */
			if ((cost1 = cost[y][x][(d+1)%4]) &&
			    (cost1+1000 < cost[y][x][d] || !cost[y][x][d]))
				{ dirty = 1; cost[y][x][d] = cost1+1000; }

			/* from left */
			if ((cost1 = cost[y][x][(d+3)%4]) &&
			    (cost1+1000 < cost[y][x][d] || !cost[y][x][d]))
				{ dirty = 1; cost[y][x][d] = cost1+1000; }
		}
	}
}

int
main(int argc, char **argv)
{
	int p1=0,p2=0, sx=0,sy=0, ex=0,ey=0, x,y;
	char *p;

	if (argc > 1)
		DISCARD(freopen(argv[1], "r", stdin));

	for (y=1; fgets(g[y]+1, GZ-1, stdin); y++) {
		if ((p = strchr(g[y]+1, 'S'))) { sy=y; sx=p-g[y]; }
		if ((p = strchr(g[y]+1, 'E'))) { ey=y; ex=p-g[y]; }
		assert(y+1 < GZ-1);
	}

	cost[sy][sx][EE] = 1;

	prune_dead();
	propagate_costs();
	prune_subopt();

	p1 = minat(ex, ey) -1;	/* costs[] values start at 1! */

	for (y=1; y<GZ-1; y++)
	for (x=1; x<GZ-1; x++)
		p2 += minat(x,y) > 0;

	printf("16: %d %d\n", p1, p2);
	return 0;
}

C#

Ended up modifying part 1 to do part 2 and return both answers at once.

using System.Collections.Immutable;
using System.Diagnostics;
using Common;

namespace Day16;

static class Program
{
    static void Main()
    {
        var start = Stopwatch.GetTimestamp();

        var smallInput = Input.Parse("smallsample.txt");
        var sampleInput = Input.Parse("sample.txt");
        var programInput = Input.Parse("input.txt");

        Console.WriteLine($"Part 1 small: {Solve(smallInput)}");
        Console.WriteLine($"Part 1 sample: {Solve(sampleInput)}");
        Console.WriteLine($"Part 1 input: {Solve(programInput)}");

        Console.WriteLine($"That took about {Stopwatch.GetElapsedTime(start)}");
    }

    static (int part1, int part2) Solve(Input i)
    {
        State? endState = null;
        Dictionary<(Point, int), int> lowestScores = new();

        var queue = new Queue<State>();
        queue.Enqueue(new State(i.Start, 1, 0, ImmutableHashSet<Point>.Empty));
        while (queue.TryDequeue(out var state))
        {
            if (ElementAt(i.Map, state.Location) is '#')
            {
                continue;
            }

            if (lowestScores.TryGetValue((state.Location, state.DirectionIndex), out var lowestScoreSoFar))
            {
                if (state.Score > lowestScoreSoFar) continue;
            }

            lowestScores[(state.Location, state.DirectionIndex)] = state.Score;

            var nextStatePoints = state.Points.Add(state.Location);

            if (state.Location == i.End)
            {
                if ((endState is null) || (state.Score < endState.Score))
                    endState = state with { Points = nextStatePoints };
                else if (state.Score == endState.Score)
                    endState = state with { Points = nextStatePoints.Union(endState.Points) };
                continue;
            }

            // Walk forward
            queue.Enqueue(state with
            {
                Location = state.Location.Move(CardinalDirections[state.DirectionIndex]),
                Score = state.Score + 1,
                Points = nextStatePoints,
            });

            // Turn clockwise
            queue.Enqueue(state with
            {
                DirectionIndex = (state.DirectionIndex + 1) % CardinalDirections.Length,
                Score = state.Score + 1000,
                Points = nextStatePoints,
            });

            // Turn counter clockwise
            queue.Enqueue(state with
            {
                DirectionIndex = (state.DirectionIndex + CardinalDirections.Length - 1) % CardinalDirections.Length,
                Score = state.Score + 1000,
                Points = nextStatePoints, 
            });
        }

        if (endState is null) throw new Exception("No end state found!");
        return (endState.Score, endState.Points.Count);
    }

    public static void DumpMap(Input i, ISet<Point>? points, Point current)
    {
        for (int row = 0; row < i.Bounds.Row; row++)
        {
            for (int col = 0; col < i.Bounds.Col; col++)
            {
                var p = new Point(row, col);
                Console.Write(
                    (p == current) ? 'X' :
                    (points?.Contains(p) ?? false) ? 'O' :
                    ElementAt(i.Map, p));
            }

            Console.WriteLine();
        }

        Console.WriteLine();
    }

    public static char ElementAt(string[] map, Point location) => map[location.Row][location.Col];

    public record State(Point Location, int DirectionIndex, int Score, ImmutableHashSet<Point> Points);

    public static readonly Direction[] CardinalDirections =
        [Direction.Up, Direction.Right, Direction.Down, Direction.Left];
}

public class Input
{
    public string[] Map { get; init; } = [];
    public Point Start { get; init; } = new(-1, -1);
    public Point End { get; init; } = new(-1, -1);
    public Point Bounds => new(this.Map.Length, this.Map[0].Length);

    public static Input Parse(string file)
    {
        var map = File.ReadAllLines(file);
        Point start = new(-1, -1), end = new(-1, -1);
        foreach (var p in map
            .SelectMany((line, i) => new []
            {
                 new Point(i, line.IndexOf('S')),
                 new Point(i, line.IndexOf('E')),
            })
            .Where(p => p.Col >= 0)
            .Take(2))
        {
            if (map[p.Row][p.Col] is 'S') start = p;
            else end = p;
        }

        return new Input()
        {
            Map = map,
            Start = start,
            End = end,
        };
    }
}

Haskell

import Control.Arrow
import Control.Monad
import Control.Monad.RWS
import Control.Monad.Trans.Maybe
import Data.Array.Unboxed
import Data.List
import Data.Map qualified as M
import Data.Maybe
import Data.Set qualified as S

data Dir = N | S | W | E deriving (Show, Eq, Ord)
type Maze = UArray Pos Char
type Pos = (Int, Int)
type Node = (Pos, Dir)
type CostNode = (Int, Node)
type Problem = RWS Maze [(Node, [Node])] (M.Map Node Int, S.Set (CostNode, Maybe Node))

parse = toMaze . lines

toMaze :: [String] -> Maze
toMaze b = listArray ((0, 0), (n - 1, m - 1)) $ concat b
  where
    n = length b
    m = length $ head b

next :: Int -> (Pos, Dir) -> Problem [CostNode]
next c (p, d) = do
    m <- ask

    let straigth = fmap ((1,) . (,d)) . filter ((/= '#') . (m !)) . return $ move d p
        turn = (1000,) . (p,) <$> rot d

    return $ first (+ c) <$> straigth ++ turn

move N = first (subtract 1)
move S = first (+ 1)
move W = second (subtract 1)
move E = second (+ 1)

rot d
    | d `elem` [N, S] = [E, W]
    | otherwise = [N, S]

dijkstra :: MaybeT Problem ()
dijkstra = do
    m <- ask
    visited <- gets fst
    Just (((cost, vertex@(p, _)), father), queue) <- gets (S.minView . snd)

    let (prevCost, visited') = M.insertLookupWithKey (\_ a _ -> a) vertex cost visited

    case prevCost of
        Nothing -> do
            queue' <- lift $ foldr S.insert queue <$> (fmap (,Just vertex) <$> next cost vertex)
            put (visited', queue')
            tell [(vertex, maybeToList father)]
        Just c -> do
            if c == cost
                then tell [(vertex, maybeToList father)]
                else guard $ m ! p /= 'E'
            put (visited, queue)
    dijkstra

solve b = do
    start <- getStart b
    end <- getEnd b
    let ((m, _), w) = execRWS (runMaybeT dijkstra) b (M.empty, S.singleton (start, Nothing))
        parents = M.fromListWith (++) w
        endDirs = (end,) <$> [N, S, E, W]
        min = minimum $ mapMaybe (`M.lookup` m) endDirs
        ends = filter ((== Just min) . (`M.lookup` m)) endDirs
        part2 =
            S.size . S.fromList . fmap fst . concat . takeWhile (not . null) $
                iterate (>>= flip (M.findWithDefault []) parents) ends
    return (min, part2)

getStart :: Maze -> Maybe CostNode
getStart = fmap ((0,) . (,E) . fst) . find ((== 'S') . snd) . assocs

getEnd :: Maze -> Maybe Pos
getEnd = fmap fst . find ((== 'E') . snd) . assocs

main = getContents >>= print . solve . parse

Haskell

This one was surprisingly slow to run

import Control.Arrow

import Data.Map (Map)
import Data.Set (Set)
import Data.Array.ST (STArray)
import Data.Array (Array)
import Control.Monad.ST (ST, runST)

import qualified Data.Char as Char
import qualified Data.List as List
import qualified Data.Map as Map
import qualified Data.Set as Set
import qualified Data.Array.ST as MutableArray
import qualified Data.Array as Array
import qualified Data.Maybe as Maybe

data Direction = East | West | South | North
        deriving (Show, Eq, Ord)
data MazeTile = Start | End | Wall | Unknown | Explored (Map Direction ExplorationScore)
        deriving Eq

--      instance Show MazeTile where
--              show Wall = "#"
--              show Start = "S"
--              show End = "E"
--              show Unknown = "."
--              show (Explored (East, _))  = ">"
--              show (Explored (South, _)) = "v"
--              show (Explored (West, _))  = "<"
--              show (Explored (North, _)) = "^"

type Position = (Int, Int)
type ExplorationScore = Int

translate '#' = Wall
translate '.' = Unknown
translate 'S' = Start
translate 'E' = End

parse :: String -> Array (Int, Int) MazeTile
parse s = Array.listArray ((1, 1), (height - 1, width)) . map translate . filter (/= '\n') $ s
        where
                width = length . takeWhile (/= '\n') $ s
                height = length . filter (== '\n') $ s

(a1, b1) .+. (a2, b2) = (a1+a2, b1+b2)
(a1, b1) .-. (a2, b2) = (a1-a2, b1-b2)

directions = [East, West, South, North]
directionVector East  = (0,  1)
directionVector West  = (0, -1)
directionVector North = (-1, 0)
directionVector South = ( 1, 0)

turnRight East  = South
turnRight South = West
turnRight West  = North
turnRight North = East

walkableNeighbors a p = do
        let neighbors = List.map ((.+. p) . directionVector) directions
        tiles <- mapM (MutableArray.readArray a) neighbors
        let neighborPosition = List.map fst . List.filter ((/= Wall). snd) . zip neighbors $ tiles
        return $ neighborPosition


findDeadEnds a = Array.assocs
        >>> List.filter (snd >>> (== Unknown))
        >>> List.map (fst)
        >>> List.filter (isDeadEnd a)
        $ a
isDeadEnd a p = List.map directionVector
        >>> List.map (.+. p)
        >>> List.map (a Array.!)
        >>> List.filter (/= Wall)
        >>> List.length
        >>> (== 1)
        $ directions

fillDeadEnds :: Array (Int, Int) MazeTile -> ST s (Array (Int, Int) MazeTile)
fillDeadEnds a = do
        ma <- MutableArray.thaw a
        let deadEnds = findDeadEnds a
        mapM_ (fillDeadEnd ma) deadEnds
        MutableArray.freeze ma

fillDeadEnd :: STArray s (Int, Int) MazeTile -> Position -> ST s ()
fillDeadEnd a p = do
        MutableArray.writeArray a p Wall
        p' <- walkableNeighbors a p >>= return . head
        t <- MutableArray.readArray a p'
        n <- walkableNeighbors a p' >>= return . List.length
        if n == 1 && t == Unknown then fillDeadEnd a p' else return ()

thawArray :: Array (Int, Int) MazeTile -> ST s (STArray s (Int, Int) MazeTile)
thawArray a = do
        a' <- MutableArray.thaw a
        return a'

solveMaze a = do
        a' <- fillDeadEnds a
        a'' <- thawArray a'
        let s = Array.assocs
                >>> List.filter ((== Start) . snd)
                >>> Maybe.listToMaybe
                >>> Maybe.maybe (error "Start not in map") fst
                $ a
        let e = Array.assocs
                >>> List.filter ((== End) . snd)
                >>> Maybe.listToMaybe
                >>> Maybe.maybe (error "End not in map") fst
                $ a
        MutableArray.writeArray a'' s $ Explored (Map.singleton East 0)
        MutableArray.writeArray a'' e $ Unknown
        solveMaze' (s, East) a''
        fa <- MutableArray.freeze a''
        t <- MutableArray.readArray a'' e
        case t of
                Wall  -> error "Unreachable code"
                Start -> error "Unreachable code"
                End   -> error "Unreachable code"
                Unknown -> error "End was not explored yet"
                Explored m -> return (List.minimum . List.map snd . Map.toList $ m, countTiles fa s e)

countTiles a s p = Set.size . countTiles' a s p $ South

countTiles' :: Array (Int, Int) MazeTile -> Position -> Position -> Direction -> Set Position
countTiles' a s p d
        | p == s    = Set.singleton p
        | otherwise = Set.unions 
                . List.map (Set.insert p) 
                . List.map (uncurry (countTiles' a s)) 
                $ (zip minCostNeighbors minCostDirections)
        where
                minCostNeighbors   = List.map ((p .-.) . directionVector) minCostDirections
                minCostDirections  = List.map fst . List.filter ((== minCost) . snd) . Map.toList $ visits
                visits = case a Array.! p of
                        Explored m -> Map.adjust (+ (-1000)) d m
                minCost = List.minimum . List.map snd . Map.toList $ visits

maybeExplore c p d a = do
        t <- MutableArray.readArray a p
        case t of
                Wall     -> return ()
                Start    -> error "Unreachable code"
                End      -> error "Unreachable code"
                Unknown  -> do
                        MutableArray.writeArray a p $ Explored (Map.singleton d c)
                        solveMaze' (p, d) a
                Explored m -> do
                        let c' = Maybe.maybe c id (m Map.!? d)
                        if c <= c' then do
                                let m' = Map.insert d c m
                                MutableArray.writeArray a p (Explored m')
                                solveMaze' (p, d) a
                        else
                                return ()

solveMaze' :: (Position, Direction) -> STArray s (Int, Int) MazeTile -> ST s ()
solveMaze' s@(p, d) a = do
        t <- MutableArray.readArray a p
        case t of
                Wall -> return ()
                Start -> error "Unreachable code"
                End -> error "Unreachable code"
                Unknown -> error "Starting on unexplored field"
                Explored m -> do
                        let c = m Map.! d
                        maybeExplore (c+1)    (p .+. directionVector d)  d a
                        let d' = turnRight d
                        maybeExplore (c+1001) (p .+. directionVector d') d' a
                        let d'' = turnRight d'
                        maybeExplore (c+1001) (p .+. directionVector d'') d'' a
                        let d''' = turnRight d''
                        maybeExplore (c+1001) (p .+. directionVector d''') d''' a

part1 a = runST (solveMaze a)

main = getContents
        >>= print
        . part1
        . parse

Dart

I liked the flexibility of the path operator in the Uiua solution so much that I built a similar search function in Dart. Not quite as compact, but still an interesting piece of code that I will keep on hand for other path-finding puzzles.

About 80 lines of code, about half of which is the super-flexible search function.

import 'dart:math';
import 'package:collection/collection.dart';
import 'package:more/more.dart';

List<Point<num>> d4 = [Point(1, 0), Point(-1, 0), Point(0, 1), Point(0, -1)];

/// Returns cost to destination, plus list of routes to destination.
/// Does Dijkstra/A* search depending on whether heuristic returns 1 or
/// something better.
(num, List<List<T>>) aStarSearch<T>(T start, Map<T, num> Function(T) fNext,
    int Function(T) fHeur, bool Function(T) fAtEnd) {
  var cameFrom = SetMultimap<T, T>.fromEntries([MapEntry(start, start)]);

  var ends = <T>{};
  var front = PriorityQueue<T>((a, b) => fHeur(a).compareTo(fHeur(b)))
    ..add(start);
  var cost = <T, num>{start: 0};
  while (front.isNotEmpty) {
    var here = front.removeFirst();
    if (fAtEnd(here)) {
      ends.add(here);
      continue;
    }
    var ns = fNext(here);
    for (var n in ns.keys) {
      var nCost = cost[here]! + ns[n]!;
      if (!cost.containsKey(n) || nCost < cost[n]!) {
        cost[n] = nCost;
        front.add(n);
        cameFrom.removeAll(n);
      }
      if (cost[n] == nCost) cameFrom[n].add(here);
    }
  }

  Iterable<List<T>> routes(T h) sync* {
    if (h == start) {
      yield [h];
      return;
    }
    for (var p in cameFrom[h]) {
      yield* routes(p).map((e) => e + [h]);
    }
  }

  var minCost = ends.map((e) => cost[e]!).min;
  ends = ends.where((e) => cost[e]! == minCost).toSet();
  return (minCost, ends.fold([], (s, t) => s..addAll(routes(t).toList())));
}

typedef PP = (Point, Point);

(num, List<List<PP>>) solve(List<String> lines) {
  var grid = {
    for (var r in lines.indexed())
      for (var c in r.value.split('').indexed().where((e) => e.value != '#'))
        Point<num>(c.index, r.index): c.value
  };
  var start = grid.entries.firstWhere((e) => e.value == 'S').key;
  var end = grid.entries.firstWhere((e) => e.value == 'E').key;
  var dir = Point<num>(1, 0);

  fHeur(PP pd) => 1; // faster than euclidean distance.
  fNextAndCost(PP pd) => <PP, int>{
        for (var n in d4
            .where((n) => n != pd.last * -1 && grid.containsKey(pd.first + n)))
          (pd.first + n, n): ((n == pd.last) ? 1 : 1001) // (Point, Dir) : Cost
      };
  fAtEnd(PP pd) => pd.first == end;

  return aStarSearch<PP>((start, dir), fNextAndCost, fHeur, fAtEnd);
}

part1(List<String> lines) => solve(lines).first;

part2(List<String> lines) => solve(lines)
    .last
    .map((l) => l.map((e) => e.first).toSet())
    .flattenedToSet
    .length;

Rust

Dijkstra’s algorithm. While the actual shortest path was not needed in part 1, only the distance, in part 2 the path is saved in the parent hashmap, and crucially, if we encounter two paths with the same distance, both parent nodes are saved. This ensures we end up with all shortest paths in the end.

use std::cmp::{Ordering, Reverse};

use euclid::{default::*, vec2};
use priority_queue::PriorityQueue;
use rustc_hash::{FxHashMap, FxHashSet};

const DIRS: [Vector2D<i32>; 4] = [vec2(1, 0), vec2(0, 1), vec2(-1, 0), vec2(0, -1)];

type Node = (Point2D<i32>, u8);

fn parse(input: &str) -> (Vec<Vec<bool>>, Point2D<i32>, Point2D<i32>) {
    let mut start = None;
    let mut end = None;
    let mut field = Vec::new();
    for (y, l) in input.lines().enumerate() {
        let mut row = Vec::new();
        for (x, b) in l.bytes().enumerate() {
            if b == b'S' {
                start = Some(Point2D::new(x, y).to_i32());
            } else if b == b'E' {
                end = Some(Point2D::new(x, y).to_i32());
            }
            row.push(b == b'#');
        }
        field.push(row);
    }
    (field, start.unwrap(), end.unwrap())
}

fn adj(field: &[Vec<bool>], (v, dir): Node) -> Vec<(Node, u32)> {
    let mut adj = Vec::with_capacity(3);
    let next = v + DIRS[dir as usize];
    if !field[next.y as usize][next.x as usize] {
        adj.push(((next, dir), 1));
    }
    adj.push(((v, (dir + 1) % 4), 1000));
    adj.push(((v, (dir + 3) % 4), 1000));
    adj
}

fn shortest_path_length(field: &[Vec<bool>], start: Node, end: Point2D<i32>) -> u32 {
    let mut dist: FxHashMap<Node, u32> = FxHashMap::default();
    dist.insert(start, 0);
    let mut pq: PriorityQueue<Node, Reverse<u32>> = PriorityQueue::new();
    pq.push(start, Reverse(0));
    while let Some((v, _)) = pq.pop() {
        for (w, weight) in adj(field, v) {
            let dist_w = dist.get(&w).copied().unwrap_or(u32::MAX);
            let new_dist = dist[&v] + weight;
            if dist_w > new_dist {
                dist.insert(w, new_dist);
                pq.push_increase(w, Reverse(new_dist));
            }
        }
    }
    // Shortest distance to end, regardless of final direction
    (0..4).map(|dir| dist[&(end, dir)]).min().unwrap()
}

fn part1(input: String) {
    let (field, start, end) = parse(&input);
    let distance = shortest_path_length(&field, (start, 0), end);
    println!("{distance}");
}

fn shortest_path_tiles(field: &[Vec<bool>], start: Node, end: Point2D<i32>) -> u32 {
    let mut parents: FxHashMap<Node, Vec<Node>> = FxHashMap::default();
    let mut dist: FxHashMap<Node, u32> = FxHashMap::default();
    dist.insert(start, 0);
    let mut pq: PriorityQueue<Node, Reverse<u32>> = PriorityQueue::new();
    pq.push(start, Reverse(0));
    while let Some((v, _)) = pq.pop() {
        for (w, weight) in adj(field, v) {
            let dist_w = dist.get(&w).copied().unwrap_or(u32::MAX);
            let new_dist = dist[&v] + weight;
            match dist_w.cmp(&new_dist) {
                Ordering::Greater => {
                    parents.insert(w, vec![v]);
                    dist.insert(w, new_dist);
                    pq.push_increase(w, Reverse(new_dist));
                }
                // Remember both parents if distance is equal
                Ordering::Equal => parents.get_mut(&w).unwrap().push(v),
                Ordering::Less => {}
            }
        }
    }
    let mut path_tiles: FxHashSet<Point2D<i32>> = FxHashSet::default();
    path_tiles.insert(end);

    // Shortest distance to end, regardless of final direction
    let shortest_dist = (0..4).map(|dir| dist[&(end, dir)]).min().unwrap();
    for dir in 0..4 {
        if dist[&(end, dir)] == shortest_dist {
            collect_tiles(&parents, &mut path_tiles, (end, dir));
        }
    }
    path_tiles.len() as u32
}

fn collect_tiles(
    parents: &FxHashMap<Node, Vec<Node>>,
    tiles: &mut FxHashSet<Point2D<i32>>,
    cur: Node,
) {
    if let Some(pars) = parents.get(&cur) {
        for p in pars {
            tiles.insert(p.0);
            collect_tiles(parents, tiles, *p);
        }
    }
}

fn part2(input: String) {
    let (field, start, end) = parse(&input);
    let tiles = shortest_path_tiles(&field, (start, 0), end);
    println!("{tiles}");
}

util::aoc_main!();

Also on github

Uiua

Uiua’s new builtin path operator makes this a breeze. Given a function that returns valid neighbours for a point and their relative costs, and another function to test whether you have reached a valid goal, it gives the minimal cost, and all relevant paths. We just need to keep track of the current direction as we work through the maze.

(edit: forgot the Try It Live! link)

Data  ← ≡°□°/$"_\n_" "#################\n#...#...#...#..E#\n#.#.#.#.#.#.#.#^#\n#.#.#.#...#...#^#\n#.#.#.#.###.#.#^#\n#>>v#.#.#.....#^#\n#^#v#.#.#.#####^#\n#^#v..#.#.#>>>>^#\n#^#v#####.#^###.#\n#^#v#..>>>>^#...#\n#^#v###^#####.###\n#^#v#>>^#.....#.#\n#^#v#^#####.###.#\n#^#v#^........#.#\n#^#v#^#########.#\n#S#>>^..........#\n#################"
D₄    ← [1_0 ¯1_0 0_1 0_¯1]
End   ← ⊢⊚=@EData
Costs ← :∩▽⟜:≡(≠@#⊡:Data⊢).≡⊟⊙⟜(+1×1000¬≡/×=)+⟜:D₄∩¤°⊟
path(Costs|≍End⊙◌°⊟)⊟:1_0⊢⊚=@SData
&p ⧻◴≡⊢/◇⊂ &p :

C#

using QuickGraph;
using QuickGraph.Algorithms.ShortestPath;

namespace aoc24;

[ForDay(16)]
public class Day16 : Solver {
  private string[] data;
  private int width, height;
  private int start_x, start_y;
  private int end_x, end_y;

  private readonly (int, int)[] directions = [(1, 0), (0, 1), (-1, 0), (0, -1)];
  private record class Edge((int, int, int) Source, (int, int, int) Target) : IEdge<(int, int, int)>;

  private DelegateVertexAndEdgeListGraph<(int, int, int), Edge> graph;
  private AStarShortestPathAlgorithm<(int, int, int), Edge> search;

  private long min_distance;
  private List<(int, int, int)> min_distance_targets;

  public void Presolve(string input) {
    data = input.Trim().Split("\n");
    width = data[0].Length;
    height = data.Length;
    for (int i = 0; i < width; i++) {
      for (int j = 0; j < height; j++) {
        if (data[j][i] == 'S') {
          start_x = i;
          start_y = j;
        } else if (data[j][i] == 'E') {
          end_x = i;
          end_y = j;
        }
      }
    }
    graph = MakeGraph();
    var start = (start_x, start_y, 0);
    search = new AStarShortestPathAlgorithm<(int, int, int), Edge>(
      graph,
      edge => edge.Source.Item3 == edge.Target.Item3 ? 1 : 1000,
      vertex => Math.Abs(vertex.Item1 - start_x) + Math.Abs(vertex.Item2 - start_y) + 1000 *
          Math.Min(vertex.Item3, 4 - vertex.Item3)
      );
    Dictionary<(int, int, int), long> distances = [];
    search.SetRootVertex(start);
    search.ExamineVertex += vertex => {
      if (vertex.Item1 == end_x && vertex.Item2 == end_y) {
        distances[vertex] = (long)search.Distances[vertex];
      }
    };
    search.Compute();
    min_distance = distances.Values.Min();
    min_distance_targets = distances.Keys.Where(v => distances[v] == min_distance).ToList();
  }

  private DelegateVertexAndEdgeListGraph<(int, int, int), Edge> MakeGraph() => new(GetAllVertices(), GetOutEdges);

  private bool GetOutEdges((int, int, int) arg, out IEnumerable<Edge> result_enumerable) {
    List<Edge> result = [];
    var (x, y, dir) = arg;
    result.Add(new Edge(arg, (x, y, (dir + 1) % 4)));
    result.Add(new Edge(arg, (x, y, (dir + 3) % 4)));
    var (tx, ty) = (x + directions[dir].Item1, y + directions[dir].Item2);
    if (data[ty][tx] != '#') result.Add(new Edge(arg, (tx, ty, dir)));
    result_enumerable = result;
    return true;
  }

  private IEnumerable<(int, int, int)> GetAllVertices() {
    for (int i = 0; i < width; i++) {
      for (int j = 0; j < height; j++) {
        if (data[j][i] == '#') continue;
        yield return (i, j, 0);
        yield return (i, j, 1);
        yield return (i, j, 2);
        yield return (i, j, 3);
      }
    }
  }

  private HashSet<(int, int, int)> GetMinimumPathNodesTo((int, int, int) vertex) {
    var (x, y, dir) = vertex;
    if (x == start_x && y == start_y && dir == 0) return [vertex];
    if (!search.Distances.TryGetValue(vertex, out var distance_to_me)) return [];
    List<(int, int, int)> candidates = [
          (x, y, (dir + 1) % 4),
          (x, y, (dir + 3) % 4),
          (x - directions[dir].Item1, y - directions[dir].Item2, dir),
      ];
    HashSet<(int, int, int)> result = [vertex];
    foreach (var (cx, cy, cdir) in candidates) {
      if (!search.Distances.TryGetValue((cx, cy, cdir), out var distance_to_candidate)) continue;
      if (distance_to_candidate > distance_to_me - (dir == cdir ? 1 : 1000)) continue;
      result = result.Union(GetMinimumPathNodesTo((cx, cy, cdir))).ToHashSet();
    }
    return result;
  }

  public string SolveFirst() => min_distance.ToString();

  public string SolveSecond() => min_distance_targets
    .SelectMany(v => GetMinimumPathNodesTo(v))
    .Select(vertex => (vertex.Item1, vertex.Item2))
    .ToHashSet()
    .Count
    .ToString();
}

Haskell

Rather busy today so late and somewhat messy! (Probably the same tomorrow…)

import Data.List
import Data.Map (Map)
import Data.Map qualified as Map
import Data.Maybe
import Data.Set (Set)
import Data.Set qualified as Set

readInput :: String -> Map (Int, Int) Char
readInput s = Map.fromList [((i, j), c) | (i, l) <- zip [0 ..] (lines s), (j, c) <- zip [0 ..] l]

bestPath :: Map (Int, Int) Char -> (Int, Set (Int, Int))
bestPath maze = go (Map.singleton start (0, Set.singleton startPos)) (Set.singleton start)
  where
    start = (startPos, (0, 1))
    walls = Map.keysSet $ Map.filter (== '#') maze
    [Just startPos, Just endPos] = map (\c -> fst <$> find ((== c) . snd) (Map.assocs maze)) ['S', 'E']
    go best edge
      | Set.null edge = Map.mapKeysWith mergePaths fst best Map.! endPos
      | otherwise =
          let nodes' =
                filter (\(x, (c, _)) -> maybe True ((c <=) . fst) $ best Map.!? x) $
                  concatMap (step . (\x -> (x, best Map.! x))) (Set.elems edge)
              best' = foldl' (flip $ uncurry $ Map.insertWith mergePaths) best nodes'
           in go best' $ Set.fromList (map fst nodes')
    step ((p@(i, j), d@(di, dj)), (cost, path)) =
      let rots = [((p, d'), (cost + 1000, path)) | d' <- [(-dj, di), (dj, -di)]]
          moves =
            [ ((p', d), (cost + 1, Set.insert p' path))
              | let p' = (i + di, j + dj),
                p `Set.notMember` walls
            ]
       in moves ++ rots
    mergePaths a@(c1, p1) b@(c2, p2) =
      case compare c1 c2 of
        LT -> a
        GT -> b
        EQ -> (c1, Set.union p1 p2)

main = do
  (score, visited) <- bestPath . readInput <$> readFile "input16"
  print score
  print (Set.size visited)

Python

Part 1: Run Dijkstra’s algorithm to find shortest path.

I chose to represent nodes using the location (i, j) as well as the direction dir faced by the reindeer.
Initially I tried creating the complete adjacency graph but that lead to max recursion so I ended up populating graph for only the nodes I was currently exploring.

Part 2: Track paths while performing Dijkstra’s algorithm.

First, I modified the algorithm to look through neighbors with equal cost along with the ones with lesser cost, so that it would go through all shortest paths.
Then, I keep track of the list of previous nodes for every node explored.
Finally, I use those lists to run through the paths backwards, taking note of all unique locations.

Code:

import os

# paths
here = os.path.dirname(os.path.abspath(__file__))
filepath = os.path.join(here, "input.txt")

# read input
with open(filepath, mode="r", encoding="utf8") as f:
    data = f.read()

from collections import defaultdict
from dataclasses import dataclass
import heapq as hq
import math

# up, right, down left
DIRECTIONS = [(-1, 0), (0, 1), (1, 0), (0, -1)]


# Represent a node using its location and the direction
@dataclass(frozen=True)
class Node:
    i: int
    j: int
    dir: int


maze = data.splitlines()
m, n = len(maze), len(maze[0])

# we always start from bottom-left corner (facing east)
start_node = Node(m - 2, 1, 1)
# we always end in top-right corner (direction doesn't matter)
end_node = Node(1, n - 2, -1)

# the graph will be updated lazily because it is too much processing
#   to completely populate it beforehand
graph = defaultdict(list)
# track nodes whose all edges have been explored
visited = set()
# heap to choose next node to explore
# need to add id as middle tuple element so that nodes dont get compared
min_heap = [(0, id(start_node), start_node)]
# min distance from start_node to node so far
# missing values are treated as math.inf
min_dist = {}
min_dist[start_node] = 0
# keep track of all previous nodes for making path
prev_nodes = defaultdict(list)


# utility method for debugging (prints the map)
def print_map(current_node, prev_nodes):
    pns = set((n.i, n.j) for n in prev_nodes)
    for i in range(m):
        for j in range(n):
            if i == current_node.i and j == current_node.j:
                print("X", end="")
            elif (i, j) in pns:
                print("O", end="")
            else:
                print(maze[i][j], end="")
        print()


# Run Dijkstra's algo
while min_heap:
    cost_to_node, _, node = hq.heappop(min_heap)
    if node in visited:
        continue
    visited.add(node)

    # early exit in the case we have explored all paths to the finish
    if node.i == end_node.i and node.j == end_node.j:
        # assign end so that we know which direction end was reached by
        end_node = node
        break

    # update adjacency graph from current node
    di, dj = DIRECTIONS[node.dir]
    if maze[node.i + di][node.j + dj] != "#":
        moved_node = Node(node.i + di, node.j + dj, node.dir)
        graph[node].append((moved_node, 1))
    for x in range(3):
        rotated_node = Node(node.i, node.j, (node.dir + x + 1) % 4)
        graph[node].append((rotated_node, 1000))

    # explore edges
    for neighbor, cost in graph[node]:
        cost_to_neighbor = cost_to_node + cost
        # The following condition was changed from > to >= because we also want to explore
        #   paths with the same cost, not just better cost
        if min_dist.get(neighbor, math.inf) >= cost_to_neighbor:
            min_dist[neighbor] = cost_to_neighbor
            prev_nodes[neighbor].append(node)
            # need to add id as middle tuple element so that nodes dont get compared
            hq.heappush(min_heap, (cost_to_neighbor, id(neighbor), neighbor))

print(f"Part 1: {min_dist[end_node]}")

# PART II: Run through the path backwards, making note of all coords

visited = set([start_node])
path_locs = set([(start_node.i, start_node.j)])  # all unique locations in path
stack = [end_node]

while stack:
    node = stack.pop()
    if node in visited:
        continue
    visited.add(node)

    path_locs.add((node.i, node.j))

    for prev_node in prev_nodes[node]:
        stack.append(prev_node)

print(f"Part 2: {len(path_locs)}")