iTD3-CLN: learn to navigate in dynamic scene through Deep Reinforcement Learning

iTD3-CLN: learn to navigate in dynamic scene through Deep Reinforcement Learning

This paper proposes iTD3-CLN, a Deep Reinforcement Learning (DRL) based low-level motion controller, to achieve map-less autonomous navigation in dynamic scene. We consider three enhancements to the Twin Delayed DDPG (TD3) for the navigation task: N-step returns, Priority Experience Replay, and a ch...

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Bibliographic Details
Main Authors: Jiang, Haoge, Esfahani, Mahdi Abolfazli, Wu, Keyu, Wan, Kong-wah, Heng, Kuan-kian, Wang, Han, Jiang, Xudong
Other Authors: School of Electrical and Electronic Engineering
Format: Journal Article
Language:English
Published: 2022
Subjects:
Engineering::Electrical and electronic engineering
Deep Reinforcement Learning
Collision Avoidance
Online Access:https://hdl.handle.net/10356/163356

Internet

https://hdl.handle.net/10356/163356

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