BEAG

Implementation of Breadth-First Exploration on Adaptive Grid for Reinforcement Learning (ICML 2024) in PyTorch. Our code is based on official implementation of DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning.

Installation

create conda environment

conda env create -n beag -f env.yaml python=3.7
conda activate beag

if permission denied,

chmod +x ./scripts/*.sh

Experiments

To reproduce our experiments, please run the script provided below
./scripts/{ENV}.sh {GPU} {SEED}

example
./scripts/Reacher.sh 0 1
./scripts/AntMaze.sh 2 3

Citation

@inproceedings{yoon2024beag,
  title={Breadth-First Exploration on Adaptive Grid for Reinforcement Learning},
  author={Yoon, Youngsik and Lee, Gangbok and Ahn, Sungsoo and Ok, Jungseul},
  booktitle={Forty-first International Conference on Machine Learning},
  year={2024}
}