GCBF-PyTorch

PyTorch Official Implementation of CoRL 2023 Paper: S Zhang, K Garg, C Fan: "Neural Graph Control Barrier Functions Guided Distributed Collision-avoidance Multi-agent Control"

!!!!!!!!!! We have improved GCBF to GCBF+!! Check out the new code here. !!!!!!!!!!

Dependencies

We recommend to use CONDA to install the requirements:

conda create -n gcbf python=3.9
conda activate gcbf
pip install -r requirements.txt

Then you need to install additional packages for torch_geometric including pyg_lib, torch_scatter, torch_sparse, torch_cluster, torch_spline_conv following the official website.

Installation

Install GCBF:

pip install -e .

Run

Environments

We provide 3 environments including SimpleCar, SimpleDrone, and DubinsCar.

Hyper-parameters

To reproduce the results shown in our paper, one can refer to hyperparams.yaml.

Train

To train the model, use:

python train.py --algo gcbf --env DubinsCar -n 16 --steps 500000

In our paper, we use 16 agents with 500000 training steps. The training logs will be saved in folder ./logs/<env>/<algo>/seed<seed>_<training-start-time>. We also provide the following flags:

• --env: environment, including SimpleCar, SimpleDrone, DubinsCar
• --algo: algorithm, including gcbf, macbf
• -n: number of agents
• --steps: number of training steps
• --batch-size: batch size
• --area-size: side length of the environment
• --obs: number of obstacles
• --seed: random seed
• --gpu: GPU ID
• --cpu: use CPU
• --log-path: path to save the training logs

By default, the training uses the hyperparameters in hyperparams.yaml. To use different hyperparameters, one can use the flag --cus and then use the flags --h-dot-coef and --action-coef to specify the new hyper-parameters.

Test

To test the learned model, use:

python test.py --path <path-to-log> --epi <number-of-episodes>

This should report the safety rate, goal reaching rate, and success rate of the learned model, and generate videos of the learned model in <path-to-log>/videos. Use the following flags to customize the test:

• -n: number of agents
• --obs: number of obstacles
• --area-size: side length of the environment
• --sense-radius: sensing radius of the agents
• --iter: number of training iterations of the model
• --epi: number of episodes to test
• --seed: random seed
• --gpu: GPU ID
• --cpu: use CPU
• --no-video: do not generate videos

There is also a nominal controller implemented for each environment for goal reaching. They can be tested using:

python test.py --env <env> -n <number-of-agents> --epi <number-of-episodes>

Pre-trained models

We provide the pre-trained models in the folder ./pretrained.

Citation

@inproceedings{zhang2023gcbf,
      title={Neural Graph Control Barrier Functions Guided Distributed Collision-avoidance Multi-agent Control},
      author={Zhang, Songyuan and Garg, Kunal and Fan, Chuchu},
      booktitle={7th Annual Conference on Robot Learning},
      year={2023}
}

Acknowledgement

The developers were partially supported by MITRE during the project.