PICTS: A Novel Deep Reinforcement Learning Approach for Dynamic P-I Control in Scanning Probe Microscopy
arXiv:2502.07326v1h-index: 22
Originality Synthesis-oriented
AI Analysis
This addresses control challenges in scanning probe microscopy, but appears incremental as it applies existing deep reinforcement learning methods to a specific domain.
The paper tackles the problem of dynamic control in scanning probe microscopy by developing a deep reinforcement learning approach, resulting in a system that adjusts control strategies in real-time.
We have developed a Parallel Integrated Control and Training System, leveraging the deep reinforcement learning to dynamically adjust the control strategies in real time for scanning probe microscopy techniques.