DRL-CEWS

This is the code accompanying the paper: "Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning Approach" by Yinuo Zhao, Chi Harold Liu, et. al, published at ICDE 2020.

📄 Description

DRL-CEWS is a novel deep reinforcement learning (DRL) approach for curiosity-driven energy-efficient worker scheduling, to achieve an optimal trade-off between maximizing the collected amount of data and coverage fairness, and minimizing the overall energy consumption of workers.

Installation

1. Clone repo

   git clone https://github.com/BIT-MCS/DRL-CEWS.git
   cd DRL-CEWS

2. Install dependent packages

   pip install -r requirements.txt
   

⚡ Quick Inference

Test the model trained with 100 PoIs (2 UAVs and 2 charging stations). Download the model from Google Driver to ckpt/. Then, change the trainable to False in the parameter configuration file /uav2_charge2/exper_dppo_curiosity/params.py. After, run the following command the test the model.

python run.py

Last, find the result under /result.

💻 Training

Change the trainable to True in the parameter configuration file /uav2_charge2/exper_dppo_curiosity/params.py and then run the following command the train the model.

python run.py

Find the result under /result.

🏁 Testing

Same as that in Quick Inference.

📜 Acknowledgement

This paper was supported by National Natural Science Foundation of China (No. 61772072).

📧 Contact

If you have any question, please email ynzhao@bit.edu.cn.

Paper

If you are interested in our work, please cite our paper as

@inproceedings{liu2020curiosity,
  title={Curiosity-driven energy-efficient worker scheduling in vehicular crowdsourcing: A deep reinforcement learning approach},
  author={Liu, Chi Harold and Zhao, Yinuo and Dai, Zipeng and Yuan, Ye and Wang, Guoren and Wu, Dapeng and Leung, Kin K},
  booktitle={2020 IEEE 36th International Conference on Data Engineering (ICDE)},
  pages={25--36},
  year={2020},
  organization={IEEE}
}