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Lyapunov Barrier Policy Optimization - Simple Pytorch implementation for Model free Safe Reinforcement Learning

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Lyapunov Barrier Policy Optimization

This code reproduces the results for our method and baselines showed in the paper.[ArXiv].

If you use this code in your research project please cite us as:

@article{sikchi2021lyapunov,
  title={Lyapunov barrier policy optimization},
  author={Sikchi, Harshit and Zhou, Wenxuan and Held, David},
  journal={arXiv preprint arXiv:2103.09230},
  year={2021}
}

Install

Instructions

  • All the experiments are to be run under the root folder.
  • Main algorithms are implemented in LBPO.py and BACKTRACK.py.

Running Experiments

python LBPO.py --env <env_name> --exp_name <experiment name>     
python BACKTRACK.py --env <env_name> --exp_name <experiment name>     

Environments

Safety environments: Safexp-{robot}{task}{difficulty}-v0
Choose robot from {Point, Car, Doggo}, task from {Goal, Push} and difficulty from {1,2}.

Important Note

Parts of the codes are used from the references mentioned below:

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