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* add minified version of https://github.com/xbandrade/sokoban-solver-generator --------- Co-authored-by: Rich Jones <miserlou@gmail.com>
52 lines
2.3 KiB
Markdown
52 lines
2.3 KiB
Markdown
# 📦 Sokoban Solver and Generator
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This folder contains a minified version of Bruno Andrade's Sokoban game, all pygame dependencies were stripped.
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The original version can be found here: [xbandrade/sokoban-solver-generator](https://github.com/xbandrade/sokoban-solver-generator)
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This is a Sokoban puzzle generator and solver that uses BFS, A* and Dijkstra search algorithms.
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`Sokoban` is a puzzle game in which the player pushes boxes around in a warehouse, trying to get every box to a goal.
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### ❕Sokoban Puzzle
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The puzzle states are stored in a matrix, and each element of the puzzle is represented by a single character in the matrix.
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```
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+ + + + + + +
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+ * - @ - X +
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+ + - @ - + +
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+ X - - - $ +
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+ + + + + + +
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```
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`*` - The player </br>
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`%` - The player on a goal </br>
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`@` - A box </br>
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`X` - A goal </br>
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`$` - A box on a goal </br>
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`+` - A wall </br>
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`-` - An empty position </br>
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A box on a goal will have its color changed to green on the game window.
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### ❕Sokoban Generator
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The generator will initially create a puzzle with a random board size, then the player and the boxes on goals will be randomly placed on the board.
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The player will only be able to pull boxes from their positions during the generation of a puzzle, breaking every wall on his way, so it is guaranteed that the puzzle will have a valid solution.
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### ❕ Sokoban Solver
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The algorithms used to implement the Sokoban puzzle solvers were `Breadth-First Search(BFS)` and `A*`.
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The `BFS` solver uses a queue to store the next states of the puzzle it needs to visit. A visited state is stored in a hashset, and BFS won't try to visit the same state twice.
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The `A*` algorithm is similar to the BFS algorithm, but it uses a priority queue instead of a queue, and it prioritizes moves that are more likely to solve the problem.
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It does so by setting costs to the puzzle state and the player's movements, punishing the player with high costs for a bad move and rewarding the player with lower costs for a good move.
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The state costs are defined by heuristic functions, and this solver was implemented with two different heuristics: the `Manhattan Distance` function and `Dijkstra` distance function.
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All three implementations check for possible deadlocks (states that are impossible to solve) before adding the new state to the queue.
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More about Sokoban: [Wikipedia Article](https://en.wikipedia.org/wiki/Sokoban)
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