Rényi State Entropy for Accelerating Exploration in Reinforcement Learning

By Mingqi Yuan, Mon-on Pun and Dong Wang

RISE is a generalized state entropy maximization method for providing high-quality intrinsic rewards. If you find this repository is useful in your research, please cite the [paper]:

@article{yuan2022r,
  title={R$\backslash$'enyi State Entropy for Exploration Acceleration in Reinforcement Learning},
  author={Yuan, Mingqi and Pun, Man-on and Wang, Dong},
  journal={arXiv preprint arXiv:2203.04297},
  year={2022}
}

Installation

• Get the repository with git:

git clone https://github.com/yuanmingqi/RISE.git

• Run the following command to get dependencies:

pip install -r requirements.txt

• Install the maze environment following:

https://github.com/MattChanTK/gym-maze

Training

Run the following command to train the model:

python main.py --action-space dis --env-name SpaceInvadersNoFrameskip-v4 --algo RISE 

Or use shell:

sh scripts/train_atari_rise.sh