Science Score: 67.0%

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    Found 2 DOI reference(s) in README
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    Links to: arxiv.org
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    Low similarity (13.1%) to scientific vocabulary
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Repository

Basic Info
  • Host: GitHub
  • Owner: LouisBouvier
  • License: mit
  • Language: Julia
  • Default Branch: main
  • Size: 12.6 MB
Statistics
  • Stars: 4
  • Watchers: 1
  • Forks: 1
  • Open Issues: 17
  • Releases: 0
Created about 4 years ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

WarcraftShortestPaths

Stable Dev Build Status Coverage Code Style: Blue

Overview

This package implements techniques of machine learning for operations research to compute shortest paths on Warcraft terrain images. It is one of the applications of our paper Learning with Combinatorial Optimization Layers: a Probabilistic Approach.

This application was introduced in this paper, with the corresponding dataset and code. It was also considered in a Learning with Differentiable Perturbed Optimizers setting, with corresponding code.

We focus on two main frameworks: learning by imitation, involving Fenchel-Young losses and perturbed maximizers, and learning from experience. Both are based on the InferOpt.jl package. We also leverage the GridGraphs.jl package to compute shortest paths on grid graphs using Dijkstra algorithm.

Get started

1) Please download this dataset and place it in the data folder of the repo. You can unzip it manually or using decompress_dataset function.

2) You can then activate the environment locally with this command:

julia using Pkg Pkg.activate(".")

Dataset overview

Each point of the dataset is linked to a (12x12) Warcraft terrain grid. It is composed of: - A color image of the Warcraft terrain of size (96x96). - The cost labels for the corresponding grid of size (12x12). - A 0-1 shortest path mask of size (12x12).

Two frameworks

The two distinct frameworks we consider are:

1) Learning by imitation: given the images and labels, learn the cost such that the labelled shortest paths are close to the shortest path computed with the given cost using Dijkstra on the Warcraft grids.

2) Learning by experience: given the images and a black-box function that computes the cost of a path on any grid, learn the cost such that the true cost of the paths computed as shortest paths with respect to the learned costs are low.

Owner

  • Name: Louis Bouvier
  • Login: LouisBouvier
  • Kind: user
  • Location: Paris area
  • Company: CERMICS, Ecole des Ponts ParisTech

PhD student in Machine Learning and Operations Research.

Citation (CITATION.bib)

@misc{WarcraftShortestPaths.jl,
	author  = {LouisBouvier <lbouvier975@gmail.com> and contributors},
	title   = {WarcraftShortestPaths.jl},
	url     = {https://github.com/LouisBouvier/WarcraftShortestPaths.jl},
	version = {v0.1.0},
	year    = {2022},
	month   = {5}
}

GitHub Events

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  • Fork event: 1
  • Create event: 1
Last Year
  • Watch event: 1
  • Member event: 1
  • Push event: 2
  • Fork event: 1
  • Create event: 1

Dependencies

.github/workflows/CI.yml actions
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  • julia-actions/julia-runtest v1 composite
  • julia-actions/setup-julia v1 composite
.github/workflows/CompatHelper.yml actions
.github/workflows/TagBot.yml actions
  • JuliaRegistries/TagBot v1 composite
.github/workflows/register.yml actions
  • julia-actions/RegisterAction latest composite