Feasibility Consistent Representation Learning for Safe Reinforcement Learning

Overview

This repo provides an official implementation of Feasibility Consistent Representation Learning for Safe Reinforcement Learning (ICML 2024). In this paper, we propose a feasibility-based representation learning method to extract safety-related features and improve the safe reinforcement learning. Our method FCSRL+TD3-Lag achieves SOTA (2024/02) performance on safety-gymnasium benchmark among model-free safe RL algorithms. See paper for more details.

framework

Installation

1) We recommend to use Anaconda or Miniconda to manage python environment. 2) Create conda env,

    cd FCSRL
    conda env create -f environment.yaml
    conda activate FCSRL

3) Install PyTorch according to your platform and cuda version. 4) Install FCSRL,

    pip install -e .

Training

To run a single experiment, take PointGoal1 for example, run

python scripts/{BASE_RL_ALG}_repr_CMDP.py --env_name SafetyPointGoal1Gymnasium-v0 --cudaid 0 --seed 100

where {BASE_RL_ALG} can be ppo or td3. For other task, you can simply replace PointGoal1 and choose a task from [PointGoal1, PointButton1, PointPush1, PointGoal2, CarGoal1, CarButton1]. You can replace --cudaid 0 to --cudaid -1 to train with CPU.

For image-based task,

python scripts/td3_repr_vision_CMDP.py --env_name SafetyPointGoal2Gymnasium-v0 --cudaid 0 --seed 100

If you need to train and render with CPU, you should modify the environment variable to

os.environ["MUJOCO_GL"] = "osmesa"
os.environ["PYOPENGL_PLATFORM"] = "osmesa"

in script. However, it can be very low if you train without GPU on image-based tasks.

Citation

If you find our work helpful, please cite:

@article{cen2024feasibility,
  title={Feasibility Consistent Representation Learning for Safe Reinforcement Learning},
  author={Cen, Zhepeng and Yao, Yihang and Liu, Zuxin and Zhao, Ding},
  journal={arXiv preprint arXiv:2405.11718},
  year={2024}
}

Acknowledgement

This repo is partly based on Tianshou.

czp16 / FCSRL