init without code

.gitignore

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+# Hydra
+outputs/
+artifacts/
+
+# Byte-compiled / optimized / DLL files
+__pycache__
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# PyCharm projects
+.idea/
+

README.md

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+# RaceMOP
+
+[![standard-readme compliant](https://img.shields.io/badge/readme%20style-standard-brightgreen.svg?style=flat-square)](https://github.com/RichardLitt/standard-readme)
+
+This repository is the official implementation of the [paper](https://scholar.google.com/citations?view_op=view_citation&hl=de&user=2ttMbLQAAAAJ&citation_for_view=2ttMbLQAAAAJ:WF5omc3nYNoC):
+
+> **RaceMOP: Mapless Online Path Planning for Multi-Agent Autonomous Racing using Residual Policy Learning**
+>
+> [Trumpp, Raphael](https://scholar.google.com/citations?user=2ttMbLQAAAAJ&hl=en), 
+> [Ehsan Javanmardi](https://scholar.google.com/citations?user=7kusI58AAAAJ&hl=en&oi=ao),
+> [Jin Nakazato](https://scholar.google.com/citations?user=7Y1CFxMAAAAJ&hl=en),
+> [Manabu Tsukada](https://scholar.google.com/citations?user=GSfYxPIAAAAJ&hl=en),
+> and [Marco Caccamo](https://scholar.google.com/citations?user=Jbo1MqwAAAAJ&hl=en&oi=ao).
+
+The paper is under review for possible publication. If you find our work useful, please consider [citing](#reference) it.
+
+**Note**: The <u>full code</u> will be made available <u>after publication</u>.
+
+<p align="center">
+  <img src="docs/anim_1.gif" alt="Replicated real-world racetracks." width="200" />
+  <img src="docs/anim_2.gif" alt="Replicated real-world racetracks." width="200" />
+  <img src="docs/anim_3.gif" alt="Replicated real-world racetracks." width="200" />
+</p>
+
+
+
+## Table of contents
+- [Background](#background)
+- [Install](#install)
+- [Usage](#usage)
+- [Reference](#reference)
+- [License](#license)
+
+## Background
+The interactive decision-making in multi-agent autonomous racing offers insights valuable beyond the domain of self-driving cars.
+Mapless online path planning is particularly of practical appeal but poses a challenge for safely overtaking opponents due to the limited planning horizon.
+Accordingly, this paper introduces RaceMOP, a novel method for mapless online path planning designed for multi-agent racing of F1TENTH cars.
+Unlike classical planners that depend on predefined racing lines, RaceMOP operates without a map, relying solely on local observations to overtake other race cars at high speed.
+Our approach combines an artificial potential field method as a base policy with residual policy learning to introduce long-horizon planning capabilities.
+We advance the field by introducing a novel approach for policy fusion with the residual policy directly in probability space.
+Our experiments for twelve simulated racetracks validate that RaceMOP is capable of long-horizon decision-making with robust collision avoidance during overtaking maneuvers.
+RaceMOP demonstrates superior handling over existing mapless planners while generalizing to unknown racetracks, paving the way for further use of our method in robotics.
+
+<p align="center">
+  <img src="docs/cover.png" alt="Architecture of the residual controller." width="400" />
+</p>
+
+## Install
+- We recommend to use a virtual environment for the installation:
+  ```bash
+  python -m venv racemop_env
+  source racemop_env/bin/activate
+  ```
+- Activate the environment and install the following packages:
+    ```bash
+  pip install torch torchvision torchaudio
+  pip install tensordict torchrl
+  pip install torchinfo
+  pip install matplotlib
+  pip install gymnasium
+  pip install hydra-core
+  pip install tqdm
+  pip install flatdict
+  pip install wandb
+  pip install numba
+  pip install cvxpy
+  pip install pyclothoids
+  pip install --upgrade "jax[cpu]"
+  pip install tensorboard
+  pip install scikit-learn
+  pip install pandas
+  pip install termcolor
+  pip install pyglet==1.5
+    ```
+- The simulator should be installed as a module:
+    ```bash
+  pip install -e simulator/
+    ```
+## Usage
+### Inference
+You can start evaluation the provided agent by running the following command:
+```bash
+python main.py
+```
+The use of your GPU can be avoided by running:
+```bash
+python main.py cuda=cpu
+```
+### Rendering
+Rendering can be enabled by setting running:
+```bash
+python main.py render=True
+```
+Since this will launch windows for all 12 maps, we recommend selecting a specific map for evaluation:
+```bash
+python main.py render=True maps.maps_train=[Catalunya] maps.maps_test=[]
+```
+
+### Others
+The baseline controller can be evaluated by running:
+```bash
+python main.py mode=baseline
+```
+
+### Docstrings
+Most of the code is documented with *automatically* generated docstrings, please use them with caution.
+
+## Reference
+If you find our work useful, please consider citing our paper:
+
+```bibtex 
+@article{trumpp2024racemop,
+  title={RaceMOP: Mapless online path planning for multi-agent autonomous racing using residual policy learning},
+  author={Trumpp, Raphael and Javanmardi, Ehsan and Nakazato, Jin and Tsukada, Manabu and Caccamo, Marco},
+  journal={arXiv preprint arXiv:2403.07129},
+  year={2024}
+}
+```
+
+## License
+[GNU General Public License v3.0 only" (GPL-3.0)](LICENSE.txt) © [raphajaner](https://github.com/raphajaner)