This is the code associated with the following publications:
Journal Version: M. Everett, Y. Chen, and J. P. How, "Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning", IEEE Access Vol. 9, 2021, pp. 10357-10377. 10.1109/ACCESS.2021.3050338, Arxiv PDF
Conference Version: M. Everett, Y. Chen, and J. P. How, "Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018. Arxiv PDF, Link to Video
This repo also contains the trained policy for the SA-CADRL paper (referred to as CADRL here) from the proceeding paper: Y. Chen, M. Everett, M. Liu, and J. P. How. “Socially Aware Motion Planning with Deep Reinforcement Learning.” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Vancouver, BC, Canada, Sept. 2017. Arxiv PDF
If you're looking to train our GA3C-CADRL policy, please see this repo instead.
Please see the documentation!
@inproceedings{Everett18_IROS,
address = {Madrid, Spain},
author = {Everett, Michael and Chen, Yu Fan and How, Jonathan P.},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
date-modified = {2018-10-03 06:18:08 -0400},
month = sep,
title = {Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning},
year = {2018},
url = {https://arxiv.org/pdf/1805.01956.pdf},
bdsk-url-1 = {https://arxiv.org/pdf/1805.01956.pdf}
}
or
@article{everett2021collision,
title={Collision avoidance in pedestrian-rich environments with deep reinforcement learning},
author={Everett, Michael and Chen, Yu Fan and How, Jonathan P},
journal={IEEE Access},
volume={9},
pages={10357--10377},
year={2021},
publisher={IEEE}
}