Abstract
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.
PICTS: A Novel Deep Reinforcement Learning Approach for Dynamic P-I Control in Scanning Probe Microscopy
Related papers
Ranked by semantic similarity β how closely each paper's abstract matches this one (100% = near-identical topic).
- Learning to Scan: A Deep Reinforcement Learning Approach for Personalized Scanning in CT Imaging (2020)64% match
- Deep Reinforcement Learning for Data-Driven Adaptive Scanning in Ptychography (2022)63% match
- Towards on-sky adaptive optics control using reinforcement learning (2022)60% match
- One-shot, Offline and Production-Scalable PID Optimisation with Deep Reinforcement Learning (2022)60% match
- A Novel Sample-efficient Deep Reinforcement Learning with Episodic Policy Transfer for PID-Based Control in Cardiac Catheterization Robots (2021)60% match
- Dynamic Angle Selection in X-Ray CT: A Reinforcement Learning Approach to Optimal Stopping (2025)60% match
- An Optical Control Environment for Benchmarking Reinforcement Learning Algorithms (2022)60% match
- Sequential Experimental Design for X-Ray CT Using Deep Reinforcement Learning (2023)60% match