Metadata-Version: 2.1
Name: gym-collision-avoidance
Version: 0.0.2
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
Description: # 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", in review, [Link to Paper](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. [Link to Paper](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. [Link to Paper](https://arxiv.org/abs/1703.08862)
        
        ---
        
        ### 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}
        }
        ```
        
Keywords: robotics planning gym rl
Platform: UNKNOWN
Requires-Python: <3.8
Description-Content-Type: text/markdown
