Learning Adaptive Safety for Multi-Agent Systems

Preprint and supplementary material available online.

Installation

The implementation has been tested with Python 3.8 under Ubuntu 20.04.

Installation:

1. Clone this repo.
2. Install requirements:

   pip install -r requirements.txt

Docker

For better reproducibility, we will release soon a Dockerfile to build a container with all the necessary dependencies. 👷

Reproducing the Results

We assume that all the experiments are run from the project directory and that the project directory is added to the PYTHONPATH environment variable as follows:

export PYTHONPATH=$PYTHONPATH:$(pwd)

Experiment 1 - End-to-End Training

1. For the multi-robot environment, run from the project directory:

./scripts/run_exp_baselines.sh [0-6]

where the exp-id [0-6] denotes runs with PPOPID, PPOLag, CPO, IPO, DDPGLag, TD3Lag, and PPOSaute respectively.

2. Similary, For the racing environment, run:

./scripts/run_exp_baselines.sh [7-13]

The results will be saved in the logs/baselines folder.

Experiment 2 - Ablation Study

We provide a couple of ablate models to augment built-in controllers with adaptive safety in the checkpoints folder.

To play with trained models with adaptive safety, run:

./scripts/run_checkpoint_eval.sh [0-1]

where the exp-id [0-1] denotes runs for particle-env and racing environments respectively.

Citation

@misc{berducci2023learning,
      title={Learning Adaptive Safety for Multi-Agent Systems}, 
      author={Luigi Berducci and Shuo Yang and Rahul Mangharam and Radu Grosu},
      year={2023},
      eprint={2309.10657},
      archivePrefix={arXiv},
      primaryClass={cs.RO}
}