Portions of the code in sagui.py are adapted from Safety Starter Agents.
Warning: If you want to use the SaGui algorithm in Safety Gym, make sure to install Safety Gym according to the instructions on the Safety Gym repo.
The various utilities here are copied over from Spinning Up in Deep RL.
To install this package:
cd /path/to/SaGui
pip install -e .
To train the guide, run:
cd /path/to/sagui
python train-guide.py --env GuideENV -s SEED --cost_lim d --logger_kwargs_str '{"output_dir": "./guide"}'
If you want to test the two versions of SaGui with a well-trained guide, run:
cd /path/to/sagui
python sagui-cs.py /path/to/guide --env StudentENV -s SEED --cost_lim d --logger_kwargs_str '{"output_dir": "./xxx"}'
python sagui-ld.py /path/to/guide --env StudentENV -s SEED --cost_lim d --logger_kwargs_str '{"output_dir": "./xxx"}'
where we should have the guide in /path/to/guide.
SEED is the random seed (we use 0, 10, ..., 90 in the paper experiments), d is the real-world safety threshold, and '{"output_dir": "./xxx"}' indicates where to store the data.
As to hyperparameters and experimental setup, you can refer to the paper and its Appendix.