Code release for Reduced Policy Optimization for Continuous Control with Hard Constraints (NeurIPS 2023).
numpy 1.21.6
torch 1.13.1
gym 0.19.0
matplotlib 3.5.2
scipy 1.9.1
scikit-learn 1.0.2
pandas 1.4.4
pygame 2.1.0
pycairo 1.11.0
igraph 0.10.8
To run the experiments, you need to first install the python package rpo via running pip install -e . in the current directory.
Then, you can simply
run python scripts/cart_exp.py to re-implement our experiments on the Safe Cartpole environment using RPODDPG algorithm.
run python scripts/pen_exp.py to re-implement our experiments on the Spring Pendulum environment using RPODDPG algorithm.
run python scripts/evopf_exp.py to re-implement our experiments on the OPF with Battery Energy Storage environment using RPODDPG algorithm.
run python scripts/cart_exp_sac.py to re-implement our experiments on the Safe Cartpole environment using RPOSAC algorithm.
run python scripts/pen_exp_sac.py to re-implement our experiments on the Spring Pendulum environment using RPOSAC algorithm.
run python scripts/evopf_exp_sac.py to re-implement our experiments on the OPF with Battery Energy Storage environment using RPOSAC algorithm.
If you find this repository useful in your research, please consider citing:
@inproceedings{
ding2023reduced,
title={Reduced Policy Optimization for Continuous Control with Hard Constraints},
author={Shutong Ding and Jingya Wang and Yali Du and Ye Shi},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=fKVEMNmWqU}
}