Learn to steer through deep reinforcement learning
It is crucial for robots to autonomously steer in complex environments safely without colliding with any obstacles. Compared to conventional methods, deep reinforcement learning-based methods are able to learn from past experiences automatically and enhance the generalization capability to cope with...
Saved in:
Main Authors: | Wu, Keyu, Esfahani, Mahdi Abolfazli, Yuan, Shenghai, Wang, Han |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Article |
Language: | English |
Published: |
2019
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/103342 http://hdl.handle.net/10220/47293 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Depth-based obstacle avoidance through deep reinforcement learning
by: Wu, Keyu, et al.
Published: (2020) -
BND*-DDQN: learn to steer autonomously through deep reinforcement learning
by: Wu, Keyu, et al.
Published: (2022) -
iTD3-CLN: learn to navigate in dynamic scene through Deep Reinforcement Learning
by: Jiang, Haoge, et al.
Published: (2022) -
Transferable deep reinforcement learning framework for autonomous vehicles with joint radar-data communications
by: Nguyen, Quang Hieu, et al.
Published: (2023) -
Deep reinforcement learning for autonomous cyber operation
by: Yong, Hou Zhong
Published: (2024)