.png){kind=icon}
.png){kind=icon}
Return
Reinforcement learning for real-time adaptive radiotherapy
.png){kind=icon}
OA
AI
K
J
D
A
D
N
H
P
DOI：10.1016/j.artmed.2026.103413.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• First application of reinforcement learning (RL) to real-time adaptive radiotherapy. • Fluence-based multi-agent reinfocement learning (MARL) tracking to improve scalability and reduce dimensionality. • Developed simulation environment for RL training in adaptive radiotherapy.
Keywords:
Reinforcement Learning
Adaptive Radiotherapy
Multi-Agent Reinforcement Learning
Real-Time Treatment
Fluence-Based Tracking
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.2
Papers:
2.5K
Citations:
7.8K
Organization
Cited Papers