This repository contains levels for boxoban, a box-pushing puzzle game inspired by Sokoban.
If you use this dataset in your work, please cite the following:
@misc{boxobanlevels,
author = {Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sebastien Racaniere, Theophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy Lillicrap, Victor Valdes},
title = {An investigation of Model-free planning: boxoban levels},
howpublished= {https://github.com/deepmind/boxoban-levels/},
year = "2018",
}
Questions regarding the dataset can be directed to Theophane Weber (theophane@google.com).
The aim is to push the boxes on top of the targets. Each level has four boxes and four targets. The player can push a box as long as nothing (another box, a wall) is behind it.
Puzzles are grouped into sets of one thousand puzzles, each set encoded as a text file. Each puzzle is a 10 by 10 ASCII string which uses the following encoding: '#' for wall, '@' for the player character, '$' for a box, and '.' for a goal position.
Within a file, puzzles are separated by a semicolon and a puzzle number. There are three levels of difficulties fo puzzles: 'unfiltered', 'medium', and 'hard'.
The unfiltered levels are separated into train (900000 puzzles), validation (100000 puzzles), and test (1000 puzzles).
Unfiltered puzzles are generated according to the procedure described in 'Imagination-Augmented Agents for Deep Reinforcement Learning' (Racaniere et. al, proceedings of NeurIPS, 2017). The unfiltered test is the one used by Orseau et al. in 'Single-Agent Policy Tree Search With Guarantees' (proceedings of NeurIPS, 2018).
The medium and hard datasets generation procedure is described in 'An investigation of model-free planning' (Guez, Mirza, Gregor, Kabra et. al, arxiv, 2018). The medium set is composed of train (450000 puzzles) and validation (50000 puzzles); the hard set has 3332 levels.
This is not an official Google product.