Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning

Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning

© 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 pro...

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Bibliographic Details
Main Authors: Semnani, Samaneh Hosseini, Liu, Hugh, Everett, Michael, De Ruiter, Anton, How, Jonathan P
Other Authors: Massachusetts Institute of Technology. Aerospace Controls Laboratory
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2021
Online Access:https://hdl.handle.net/1721.1/135355

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