An autonomous radiation source detection policy based on deep reinforcement learning with generalized ability in unknown environments
Autonomous radiation source detection has long been studied for radiation emergencies. Compared to conventional data-driven or path planning methods, deep reinforcement learning shows a strong capacity in source detection while still lacking the generalized ability to the geometry in unknown environ...
Main Authors: | Hao Hu, Jiayue Wang, Ai Chen, Yang Liu |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2023-01-01
|
Series: | Nuclear Engineering and Technology |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S1738573322004429 |
Similar Items
-
Double Q-Learning for Radiation Source Detection
by: Zheng Liu, et al.
Published: (2019-02-01) -
Hierarchical Reinforcement Learning: A Survey and Open Research Challenges
by: Matthias Hutsebaut-Buysse, et al.
Published: (2022-02-01) -
Proximal Policy Optimization for Radiation Source Search
by: Philippe Proctor, et al.
Published: (2021-09-01) -
Event-Triggered Hierarchical Planner for Autonomous Navigation in Unknown Environment
by: Changhao Chen, et al.
Published: (2023-11-01) -
Guaranteed hierarchical reinforcement learning
by: Ang, Riley Xile
Published: (2024)