https://github.com/bonifatius94/robot-sf
training environment for the simulation of a robot moving in a pedestrian-filled space
https://github.com/bonifatius94/robot-sf
gym-environment ppo reinforcement-learning self-driving-car simulation social-force-model stable-baselines3
Last synced: 2 months ago
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training environment for the simulation of a robot moving in a pedestrian-filled space
- Host: GitHub
- URL: https://github.com/bonifatius94/robot-sf
- Owner: Bonifatius94
- License: gpl-3.0
- Created: 2023-08-01T15:05:36.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-12-14T13:53:25.000Z (over 2 years ago)
- Last Synced: 2025-02-25T06:18:26.683Z (over 1 year ago)
- Topics: gym-environment, ppo, reinforcement-learning, self-driving-car, simulation, social-force-model, stable-baselines3
- Language: Python
- Homepage:
- Size: 4.4 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# robot-sf
## About
This project provides a training environment for the simulation of a robot moving
in a pedestrian-filled space.
The project interfaces with OpenAI Gym to facilitate trainings with various
SOTA reinforcement learning algorithms like e.g. StableBaselines3.
For simulating the pedestrians, the SocialForce model is used via a dependency
on a fork of PySocialForce.
Following video outlines some training results where a robot with e-scooter
kinematics is driving at the campus of University of Augsburg using real
map data from OpenStreetMap.
## Quickstart
### 1. Clone Source Code
```sh
git clone --recurse-submodules https://github.com/Bonifatius94/robot-sf
cd robot-sf
```
### 2. Install Dependencies
```sh
python3 -m pip install pip --upgrade
python3 -m pip install -r requirements.txt
python3 -m pip install -r fast-pysf/requirements.txt
```
### 3. Register *robot_sf* and *pysocialforce* Packages
```sh
pushd ./fast-pysf
python3 -m pip install .
popd
python3 -m pip install .
```
*Note: This needs to be repeated to propagate robot_sf changes on a host deployment.
Otherwise the calling scope might run an old version of robot_sf.
Dockerized deployments will recognize when a change requires a rebuild.*
Alternatively create soft links for fast-pysf. The modules and tests are
automatically detected by the interpreter if the calling scope is located
at the repository's root directory which is very useful e.g. for debugging.
```sh
ln -s fast-pysf/pysocialforce pysocialforce
pushd tests
ln -s ../fast-pysf/tests pysf_tests
popd
```
*Note: The outlined command might differ on Windows, e.g. try mklink*
### 4. Run Linter / Tests
```sh
python3 -m pytest tests
python3 -m pylint robot_sf
```
### 5. Run Visual Debugging of Pre-Trained Demo Models
```sh
python3 demo_offensive.py
python3 demo_defensive.py
```
### 6. Run StableBaselines Training (Docker)
```sh
docker-compose build && docker-compose run \
robotsf-cuda python ./scripts/training_ppo.py
```
*Note: See [this setup](./docs/GPU_SETUP.md) to install Docker with GPU support.*
### 7. Edit Maps
```sh
sudo apt-get update && sudo apt-get install -y python3-tk
```
```sh
python3 -m map_editor
```
*Note: See [this documentation](./docs/MAP_EDITOR_USAGE.md) on how to use the map editor.*
### 8. Optimize Training Hyperparams (Docker)
```sh
docker-compose build && docker-compose run \
robotsf-cuda python ./scripts/hparam_opt.py
```