- Notifications
You must be signed in to change notification settings - Fork6
JAX-based implementation for multi-agent path planning (MAPP) in continuous spaces.
License
omron-sinicx/jaxmapp
Folders and files
Name | Name | Last commit message | Last commit date | |
---|---|---|---|---|
Repository files navigation
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), Mai Nishimura (@denkiwakame)
The code has been tested on Ubuntu >=16.04 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 machine-learning models. We also provide Dockerfile to replicate our setup.
$python -m venv .venv$source .venv/bin/activate(.venv) $ pip install -e .[dev]
$docker-compose build$docker-compose up -d dev$docker-composeexec dev bash
$docker-compose up -d dev-gpu$docker-composeexec dev-gpu bash
and update JAX modules in the container...
#pip install --upgrade"jax[cuda]==0.3.16" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
- 1. Quickstart: Create MAPP problems and solve them using default roadmap construction methods
- 2. Develop roadmap construction methods
- 3. Benchmarking roadmap construction methods
- 4. Training sampler
@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}}
About
JAX-based implementation for multi-agent path planning (MAPP) in continuous spaces.
Topics
Resources
License
Uh oh!
There was an error while loading.Please reload this page.
Stars
Watchers
Forks
Releases
Packages0
Uh oh!
There was an error while loading.Please reload this page.
Contributors2
Uh oh!
There was an error while loading.Please reload this page.