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

• المؤلفون الرئيسيون: Jiang, Haoge, Esfahani, Mahdi Abolfazli, Wu, Keyu, Wan, Kong-wah, Heng, Kuan-kian, Wang, Han, Jiang, Xudong
• مؤلفون آخرون: School of Electrical and Electronic Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2022
• الموضوعات: Engineering::Electrical and electronic engineering; Deep Reinforcement Learning; Collision Avoidance
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/163356
• المؤسسة: Nanyang Technological University

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...