Metadata-Version: 2.1
Name: gym-collision-avoidance
Version: 0.0.2.post2
Summary: Simulation environment for collision avoidance
Home-page: https://github.com/mit-acl/gym-collision-avoidance
Author: Michael Everett, Yu Fan Chen, Jonathan P. How, MIT
License: UNKNOWN
Keywords: robotics planning gym rl
Platform: UNKNOWN
Requires-Python: <3.8
Description-Content-Type: text/markdown
Requires-Dist: tensorflow (<2.0,>=1.14)
Requires-Dist: Pillow
Requires-Dist: PyOpenGL
Requires-Dist: pyyaml
Requires-Dist: matplotlib
Requires-Dist: pytz
Requires-Dist: imageio (==2.4.1)
Requires-Dist: gym
Requires-Dist: moviepy
Requires-Dist: pandas

# gym-collision-avoidance

<img src="docs/_static/combo.gif" alt="Agents spelling ``CADRL''">

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}
}
```


