Code implementation for distributed Potential-ADMM

Code for the paper: "Efficient and Distributed Multi-Agent Interactive Trajectory Optimization via ADMM and Dynamic Potential Games"

Installation

You can recreate the python environment used to run these files via:

conda create --name <env> --file requirements.txt

Similiarily, using a standard Python 3 installation, you can also use:

python3 -m venv env
source env/bin/activate
pip install -r pipRequirements.txt

Simulations

To re-run the comparison between potential-ADMM and distributed potential iLQR, please download the dp-ilqr repo here