Event-triggered deep Q-network (ETDQN) for Autonomous Driving

Code for paper:
J. Lu, L. Han, Q. Wei, X. Wang, X. Dai, F.-Y. Wang. Event-triggered deep reinforcement learning using parallel control: A case study in autonomous driving. IEEE Transactions on Intelligent Vehicles, 2023, doi: 10.1109/TIV.2023.3262132.

Description

Investigate the problem of event-triggered deep reinforcement learning with discrete action space and develop an ETDQN for autonomous driving, without training an explicit triggering condition. The implicit triggering condition and the control policy share neural network parameters.

How to run

• create an anaconda environment via: conda create -n etdqn pytorch=1.12.1 python=3.9.13
• activate the anaconda environment via: conda activate etdqn
• install the required packages via: pip install -r requirements.txt
• train ETDQN via: etdqn_train.py
• test ETDQN via: etdqn_test.py
  

Citation

@article{lu2023event,
  title={Event-Triggered Deep Reinforcement Learning Using Parallel Control: A Case Study in Autonomous Driving},
  author={Lu, Jingwei and Han, Liyuan and and Wei, Qinglai and Wang, Xiao and Dai, Xingyuan and Wang, Fei-Yue}
  journal={IEEE Transactions on Intelligent Vehicles}
  doi = {10.1109/TIV.2023.3262132}
  year={2023}
}