Path Planning using Neural A* Search (ICML 2021)

This is the official repository for the following paper:

Ryo Yonetani*, Tatsunori Taniai*, Mohammadamin Barekatain, Mai Nishimura, Asako Kanezaki, "Path Planning using Neural A* Search", ICML, 2021 [paper] [project page]

TL;DR

Neural A* is a novel data-driven search-based planner that consists of a trainable encoder and a differentiable version of A* search algorithm called differentiable A* module. Neural A* learns from demonstrations to improve the trade-off between search optimality and efficiency in path planning and also to enable the planning directly on raw image inputs.

A* search	Neural A* search	Planning on raw image input

Overview

• This branch presents minimal working examples for training Neural A* to (1) solve shortest path problems and (2) perform planning directly on WarCraft map images.
• For reproducing experiments in our ICML'21 paper, please refer to icml2021 branch.
• For creating datasets used in our experiments, please visit planning datasets repository.

Getting started

• Try Neural A* on Google Colab!
• The code has been tested on Ubuntu >=18.04 as well as WSL2 (Ubuntu 20.04) on Windows 11, with python3 (>=3.8). Planning can be performed only on the CPU, and the use of GPUs is supported for training/evaluating Neural A* models. We also provide Dockerfile and docker-compose.yaml to replicate our setup.

Installation (venv)

$ git clone --recursive https://github.com/omron-sinicx/neural-astar
$ python -m venv .venv
$ source .venv/bin/activate
(.venv) $ pip install .[dev]

or with docker compose

$ docker compose build
$ docker compose up -d neural-astar
$ docker compose exec neural-astar bash

Perform shortest path search with Neural A*

See notebooks/example.ipnyb for how it works.

Training

(.venv) $ python scripts/train.py

You can also visualize and save planning results as gif.

(.venv) $ python scripts/create_gif.py

Perform planning on WarCraft data [2] with Neural A*

Preparation

• Download warcraft_maps.tar.gz from Blackbox Combinatorial Solvers page. [2]
• Extract the directory named 12x12 (smallest maps) and place it on the root of this project directory.

Training

(.venv) $ python scripts/train_warcraft.py

Once training has been done, open notebooks/example_warcraft.ipnyb to see how it works.

FAQs

Data format (c.f. #1 (comment))

The datafile mazes_032_moore_c8.npz was created using our data generation script in a separate repository https://github.com/omron-sinicx/planning-datasets.

In the data, arr_0 - arr_3 are 800 training, arr_4 - arr_7 are 100 validation, and arr_8 - arr_11 are 100 test data, which contain the following information (see also https://github.com/omron-sinicx/planning-datasets/blob/68e182801fd8cbc4c25ccdc1b14b8dd99d9bbc73/generate_spp_instances.py#L50-L61):

• arr_0, arr_4, arr_8: binary input maps
• arr_1, arr_5, arr_9: one-hot goal maps
• arr_2, arr_6, arr_10: optimal directions (among eight directions) to reach the goal
• arr_3, arr_7, arr_11: shortest distances to the goal

For each problem instance, the start location is generated randomly when __getitem__ is called:

neural-astar/neural_astar/utils/data.py

Line 114 in e6e626c

start_map = self.get_random_start_map(opt_dist)

Third-party implementations

• shreya-bhatt27/NeuralAstar-ported: Pytorch Lightning implementation with some additional experiments. See also their preprint.

Citation

# ICML2021 version
@InProceedings{pmlr-v139-yonetani21a,
  title =      {Path Planning using Neural A* Search},
  author    = {Ryo Yonetani and
               Tatsunori Taniai and
               Mohammadamin Barekatain and
               Mai Nishimura and
               Asako Kanezaki},
  booktitle =      {Proceedings of the 38th International Conference on Machine Learning},
  pages =      {12029--12039},
  year =      {2021},
  editor =      {Meila, Marina and Zhang, Tong},
  volume =      {139},
  series =      {Proceedings of Machine Learning Research},
  month =      {18--24 Jul},
  publisher =    {PMLR},
  pdf =      {http://proceedings.mlr.press/v139/yonetani21a/yonetani21a.pdf},
  url =      {http://proceedings.mlr.press/v139/yonetani21a.html},
}

# arXiv version
@article{DBLP:journals/corr/abs-2009-07476,
  author    = {Ryo Yonetani and
               Tatsunori Taniai and
               Mohammadamin Barekatain and
               Mai Nishimura and
               Asako Kanezaki},
  title     = {Path Planning using Neural A* Search},
  journal   = {CoRR},
  volume    = {abs/2009.07476},
  year      = {2020},
  url       = {https://arxiv.org/abs/2009.07476},
  archivePrefix = {arXiv},
  eprint    = {2009.07476},
  timestamp = {Wed, 23 Sep 2020 15:51:46 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2009-07476.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Acknowledgments

This repository includes some code from RLAgent/gated-path-planning-networks [1] with permission of the authors and from martius-lab/blackbox-backprop [2].

References

• [1] Lisa Lee*, Emilio Parisotto*, Devendra Singh Chaplot, Eric Xing, Ruslan Salakhutdinov, "Gated Path Planning Networks", ICML, 2018.
• [2] Marin Vlastelica Pogančić, Anselm Paulus, Vit Musil, Georg Martius, Michal Rolinek, "Differentiation of Blackbox Combinatorial Solvers", ICLR, 2020.