BND*-DDQN: learn to steer autonomously through deep reinforcement learning
It is vital for mobile robots to achieve safe autonomous steering in various changing environments. In this paper, a novel end-to-end network architecture is proposed for mobile robots to learn steering autonomously through deep reinforcement learning. Specifically, two sets of feature representatio...
Saved in:
Main Authors: | Wu, Keyu, Wang, Han, Esfahani, Mahdi Abolfazli, Yuan, Shenghai |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Article |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/159818 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Learn to steer through deep reinforcement learning
by: Wu, Keyu, et al.
Published: (2019) -
Depth-based obstacle avoidance through deep reinforcement learning
by: Wu, Keyu, et al.
Published: (2020) -
AbolDeepIO : a novel deep inertial odometry network for autonomous vehicles
by: Esfahani, Mahdi Abolfazli, et al.
Published: (2022) -
Deep learning based monocular visual-inertial odometry
by: Mahdi Abolfazli Esfahani
Published: (2021) -
TDPP-Net : achieving three-dimensional path planning via a deep neural network architecture
by: Wu, Keyu, et al.
Published: (2020)