Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning
Name
2001.06627.pdf
Description
Submitted version
Size
831.42 KB
Format
Adobe PDF
Checksum (MD5)
0b42e55155a10a5c5186ff5f880fd2a1
Author(s)
Semnani, Samaneh Hosseini
Liu, Hugh
Everett, Michael
De Ruiter, Anton
How, Jonathan P
Date Issued
2020
Journal
IEEE Robotics and Automation Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Version
Original manuscript
Abstract
© 2016 IEEE. This letter introduces a hybrid algorithm of deep reinforcement learning (RL) and Force-based motion planning (FMP) to solve distributed motion planning problem in dense and dynamic environments. Individually, RL and FMP algorithms each have their own limitations. FMP is not able to produce time-optimal paths and existing RL solutions are not able to produce collision-free paths in dense environments. Therefore, we first tried improving the performance of recent RL approaches by introducing a new reward function that not only eliminates the requirement of a pre supervised learning (SL) step but also decreases the chance of collision in crowded environments. That improved things, but there were still a lot of failure cases. So, we developed a hybrid approach to leverage the simpler FMP approach in stuck, simple and high-risk cases, and continue using RL for normal cases in which FMP can't produce optimal path. Also, we extend GA3C-CADRL algorithm to 3D environment. Simulation results show that the proposed algorithm outperforms both deep RL and FMP algorithms and produces up to 50$\%$ more successful scenarios than deep RL and up to 75$\%$ less extra time to reach goal than FMP.
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/LRA.2020.2974695
