This repository contains the official implementation for our paper: Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning.

In this work, we propose a novel framework — Wake-Sleep Option (WS-Option) — to solve Sequential Stochastic Combinatorial Optimization (SSCO) problems. Our method leverages hierarchical reinforcement learning to efficiently explore the solution space and handle the combinatorial nature of sequential decisions.

Getting Started

To reproduce our results, navigate to the specific environment folder (IM or RPP) depending on the task:

cd IM  # or cd RPP

Then simply run:

python Main.py

Note: Each task environment may have slight differences in configuration or setup.

Citation

If you find this repository helpful in your research, please consider citing our paper:

@article{feng2025sequential,
  title={Sequential Stochastic Combinatorial Optimization Using Hierarchical Reinforcement Learning},
  author={Feng, Xinsong and Yu, Zihan and Xiong, Yanhai and Chen, Haipeng},
  journal={arXiv preprint arXiv:2305.19450},
  year={2025}
}