JAXMAPP: JAX-based Library for Multi-Agent Path Planning in Continuous Spaces

Related tags

Deep Learning deep-learning mapf jax
Overview

JAXMAPP: JAX-based Library for Multi-Agent Path Planning in Continuous Spaces

gallery roadmap metrics

JAXMAPP is a JAX-based library for multi-agent path planning (MAPP) in continuous spaces, with a particular emphasis on roadmap construction and evaluation. With JAXMAPP, You can:

  • Create MAPP problem instances with homogeneous/heterogeneous agents
  • Construct roadmaps and perform prioritized planning to solve MAPP
  • Develop and evaluate your own roadmap construction methods

Main contributors: Ryo Yonetani (@yonetaniryo), Keisuke Okumura (@Kei18)

Installation

The code has been tested on Ubuntu 16.04 and 18.04, as well as WSL2 (Ubuntu 20.04) on Windows 11. Planning can be performed only on the CPU, and the use of GPUs is also supported for training/evaluating machine-learning models. We also provide Dockerfile to replicate our setup.

venv

$ python -m venv .venv
$ source .venv/bin/activate
(.venv) $ make

Docker container

$ docker-compose build
$ docker-compose up -d dev
$ docker-compose exec dev bash

Docker container with CUDA enabled

$ docker-compose up -d dev-gpu
$ docker-compose exec dev-gpu bash

and update JAX modules in the container...

# pip install "jax[cuda]==0.2.25" -f https://storage.googleapis.com/jax-releases/jax_releases.html

Tutorials

Citation

@misc{jaxmapp_2022,
author = {Yonetani, Ryo and Okumura, Keisuke},
month = {2},
title = {JAXMAPP: JAX-based Library for Multi-Agent Path Planning in Continuous Spaces},
url = {https://github.com/omron-sinicx/jaxmapp},
year = {2022}
}


@inproceedings{okumura2022ctrm,
title={CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces},
author={Okumura, Keisuke and Yonetani, Ryo and Nishimura, Mai and Kanezaki, Asako},
booktitle={Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
year={2022}
}
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Comments
  • fix bug of valid_linear_move

    fix bug of valid_linear_move

    Current valid_linear_move in jaxmapp.roadmap.utils incorrectly evaluate the validity when (1) agent is much smaller compared to its max_speed and (2) there is an obstacle around the destination of the movement. This is due to the fixed number of evaluations (num_max_steps=20) starting from the src point. Fix this problem by using jax.lax.while_loop.

    opened by yonetaniryo 0
  • Update build system

    Update build system

    This PR includes:

    • Update build system such that the whole jaxmapp package can be installed via pip, i.e., without additional make to compile cython module (@denkiwakame )
    • Minor fixes to support the latest version of jax (@yonetaniryo )
    opened by yonetaniryo 0
  • Separate dataset

    Separate dataset

    This PR separates data directory into a separate repository (omron-sinicx/jaxmapp-datasets) and allows user to download them, in addition to training data that requires a lot of time to create from scratch.

    opened by yonetaniryo 0
  • jax.numpy.nonzero() is very slow in jax>=0.2.26

    jax.numpy.nonzero() is very slow in jax>=0.2.26

    Found that jax.numpy.nonzero becomes very slow in from jax>=0.2.26, significantly impacting the runtime when trying to update the version of jax.

    Right now nonzero function is used in generate_from_nontimed_roadmap and generate_from_timed_roadmap in TimedRoadmap, e.g. https://github.com/omron-sinicx/jaxmapp/blob/109c9b71a13fad59788a5ed34e663d4430da8563/jaxmapp/roadmap/timed_roadmap.py#L107

    Replacing this with np.argwhere (acceptable because these two functions are not jit-compiled) would resolve the problem.

    opened by yonetaniryo 0
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OMRON SINIC X
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