Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
Name
1805.01956.pdf
Description
Submitted version
Size
2.42 MB
Format
Adobe PDF
Checksum (MD5)
70f9056e250feb31a19fde2653c47c5f
Author(s)
Everett, Michael
Chen, Yu Fan
How, Jonathan P.
Date Issued
October 2018
Publisher
IEEE
Citation
Everett, Michael, Chen, Yu Fan and How, Jonathan P. 2018. "Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning."
Version
Original manuscript
Abstract
© 2018 IEEE. Robots that navigate among pedestrians use collision avoidance algorithms to enable safe and efficient operation. Recent works present deep reinforcement learning as a framework to model the complex interactions and cooperation. However, they are implemented using key assumptions about other agents' behavior that deviate from reality as the number of agents in the environment increases. This work extends our previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules. This work also introduces a strategy using LSTM that enables the algorithm to use observations of an arbitrary number of other agents, instead of previous methods that have a fixed observation size. The proposed algorithm outperforms our previous approach in simulation as the number of agents increases, and the algorithm is demonstrated on a fully autonomous robotic vehicle traveling at human walking speed.
MIT Department
Massachusetts Institute of Technology. Aerospace Controls Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iros.2018.8593871
