NovelD: A Simple yet Effective Exploration Criterion

Related tags

Deep LearningNovelD
Overview

NovelD: A Simple yet Effective Exploration Criterion

Intro

This is an implementation of the method proposed in

NovelD: A Simple yet Effective Exploration Criterion and BeBold: Exploration Beyond the Boundary of Explored Regions

Citation

If you use this code in your own work, please cite our paper:

@article{zhang2021noveld,
  title={NovelD: A Simple yet Effective Exploration Criterion},
  author={Zhang, Tianjun and Xu, Huazhe and Wang, Xiaolong and Wu, Yi and Keutzer, Kurt and Gonzalez, Joseph E and Tian, Yuandong},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}

or

@article{zhang2020bebold,
  title={BeBold: Exploration Beyond the Boundary of Explored Regions},
  author={Zhang, Tianjun and Xu, Huazhe and Wang, Xiaolong and Wu, Yi and Keutzer, Kurt and Gonzalez, Joseph E and Tian, Yuandong},
  journal={arXiv preprint arXiv:2012.08621},
  year={2020}
}

Installation

# Install Instructions
conda create -n ride python=3.7
conda activate noveld 
git clone [email protected]:tianjunz/NovelD.git
cd NovelD
pip install -r requirements.txt

Train NovelD on MiniGrid

OMP_NUM_THREADS=1 python main.py --model bebold --env MiniGrid-ObstructedMaze-2Dlhb-v0 --total_frames 500000000 --intrinsic_reward_coef 0.05 --entropy_cost 0.0005

Acknowledgements

Our vanilla RL algorithm is based on RIDE.

License

This code is under the CC-BY-NC 4.0 (Attribution-NonCommercial 4.0 International) license.

Owner
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