https://github.com/mit-acl/gym-collision-avoidance
https://github.com/mit-acl/gym-collision-avoidance
Last synced: 10 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/mit-acl/gym-collision-avoidance
- Owner: mit-acl
- License: mit
- Created: 2020-02-18T22:52:24.000Z (over 6 years ago)
- Default Branch: release
- Last Pushed: 2023-04-28T18:18:24.000Z (about 3 years ago)
- Last Synced: 2024-06-17T22:48:41.464Z (about 2 years ago)
- Language: OpenEdge ABL
- Size: 18.9 MB
- Stars: 240
- Watchers: 6
- Forks: 74
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# gym-collision-avoidance
Updates:
- **2023-04-28:** Updated to be compatible with Python 3.10 and tensorflow 2. Corresponding v0.0.3 available on pypi as well, if you do not intend to modify the source code (`python -m pip install gym-collision-avoidance`)
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](http://doi.org/10.1109/ACCESS.2021.3050338), [Arxiv PDF](https://arxiv.org/abs/1910.11689)
**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](https://arxiv.org/abs/1805.01956), [Link to Video](https://www.youtube.com/watch?v=XHoXkWLhwYQ)
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](https://arxiv.org/abs/1703.08862)
If you're looking to train our GA3C-CADRL policy, please see [this repo](https://github.com/mit-acl/rl_collision_avoidance) instead.
---
### About the Code
Please see [the documentation](https://gym-collision-avoidance.readthedocs.io/en/latest/)!
### If you find this code useful, please consider citing:
```
@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}
}
```