WCSAC

Portions of the code in saclag.py and wcsac.py are adapted from Safety Starter Agents.

Warning: If you want to use the WCSAC 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. Two custom environments (StaticEnv-v0 and DynamicEnv-v0) are created within wcsac.py and saclag.py.

Installation & Operation

Two ways to install this package:

Direct installation

To install this package:

git clone https://github.com/AlgTUDelft/WCSAC.git

cd wcsac

pip install -e .

Through [Safety Starter Agents]

So you can install Safety Starter Agents, and add wcsac.py and saclag.py of our package to /path/to/safety-starter-agents/safe_rl/sac. Then you can follow the instructions on the Safety Starter Agents to use our algorithms as using their given baselines.

Reproduce Experiments

Example: If you want to test WCSAC with risk level 0.5 in StaticEnv-v0, run:

cd /path/to/wcsac

python wcsac.py --env 'StaticEnv-v0' -s SEED --cl 0.5 --cost_lim d --logger_kwargs_str '{"output_dir": "./xxx"}'

where SEED is the random seed, d is the real-world safety threshold, and '{"output_dir": "./xxx"}' indicates where to store the data.

Cite the Paper

If you use WCSAC code in your paper, please cite:

@inproceedings{WCSAC,
  title     = {{WCSAC: Worst-Case Soft Actor Critic for Safety-Constrained Reinforcement Learning}},
  author    = {
      Yang, Qisong and 
      Simão, Thiago D. and
      Tindemans, Simon H. and
      Spaan, Matthijs T. J.
  },
  booktitle = {Thirty-Fifth AAAI Conference on Artificial Intelligence},
  year      = {2021}
}

AndyYue1893 / WCSAC