Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificates

Zengyi Qin, Kaiqing Zhang, Yuxiao Chen, Jingkai Chen, Chuchu Fan

This repository contains the official implementation of Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificates published at the International Conference on Learning Representations (ICLR), 2021.

Installation

Create a virtual environment with Anaconda:

conda create -n macbf python=3.6

Activate the virtual environment:

source activate macbf

Clone this repository:

git clone https://github.com/Zengyi-Qin/macbf.git

Enter the main folder and install the dependencies:

pip install -r requirements.txt

Cars

In cars, we provide a multi-agent collision avoidance example with the double integrator dynamics. First enter the directory:

cd cars

To evaluate the pretrained neural network CBF and controller, run:

python evaluate.py --num_agents 32 --model_path models/model_save --vis 1

--num_agents specifies the number of agents in the environment. --model_path points to the prefix of the pretrained neural network weights. The visualization is disabled by default and will be enabled when --vis is set to 1.

To train the neural network CBF and controller from scratch, run:

python train.py --num_agents 32

We can add another argument --model_path and point to a pretrained model we want to use.

Drones

In drones, we consider the drone dynamics with 8-dimsional state space. Details of the dynamics can be found in Appendix C of our paper. To experiment with this example, first enter the directory:

cd drones

To evaluate the pretrained neural network CBF and controller, run:

python evaluate.py --num_agents 32 --model_path models/model_save --vis 1

To train the neural network CBF and controller from scratch, run:

python train.py --num_agents 32

The arguments are the same as the cars example.