This project provides the implementation of Learning Shared Safety Constraints from Multi-Task Demonstrations.
If you found this repository useful in your research, please consider citing our paper:
@misc{kim2023learning,
title={Learning Shared Safety Constraints from Multi-task Demonstrations},
author={Konwoo Kim and Gokul Swamy and Zuxin Liu and Ding Zhao and Sanjiban Choudhury and Zhiwei Steven Wu},
year={2023},
eprint={2309.00711},
archivePrefix={arXiv},
primaryClass={cs.LG}
}The high-level structure of this repository is as follows:
├── mticl # package folder
│ ├── saferl # safe RL library
│ ├── script # training, testing, plotting scripts
│ ├── ├── train/test_cpo.py # CRL
│ ├── ├── train_baseline.py # Chou et al. baseline
│ ├── ├── train/test_icl.py # Single-task ICL
│ ├── ├── planner_icl.py # Multi-task ICL
│ ├── utils # utility functions
| ├── demos # generated expert demos
│ ├── plots # plots for the paper
│ ├── experiments # experiments for the paper
├── experts # expert policies trained with CRL
├── learners # learner policies trained with ICL
Note
Please see here for a detailed overview of the codebase.
conda create -n mticl python=3.10.8
conda activate mticl
pip install -r requirements.txt
export PYTHONPATH=mticl:$PYTHONPATH
Important
All scripts should be run from under mticl/.
Scripts for replicating results from the paper are provided under the experiments/ directory.
./experiments/pybullet_baseline.sh
./experiments/pybullet_icl.sh
./experiments/pybullet_noisy.sh
./experiments/ant_maze.sh
The core saferl/ code was developed from an early version of the FSRL repository which provides fast, high-quality implementations of safe RL algorithms.
Warning
This code is not fully compatible with the latest version of FSRL. See here for more information.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent