# Deep Reinforcement Learning for Data-Driven Adaptive Scanning in Ptychography

arXiv: https://arxiv.org/abs/2203.15413 · HTML: https://mlsanity.com/paper/2203.15413/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 70 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [Recurrent Models of Visual Attention](https://mlsanity.com/paper/1406.6247/) (2014)
- [Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines](https://mlsanity.com/paper/1803.07246/) (2018)
- [Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial](https://mlsanity.com/paper/2011.03615/) (2020)
- [Action Recognition using Visual Attention](https://mlsanity.com/paper/1511.04119/) (2015)
- [Difference Rewards Policy Gradients](https://mlsanity.com/paper/2012.11258/) (2020)
- [Low-dose cryo electron ptychography via non-convex Bayesian optimization](https://mlsanity.com/paper/1702.05732/) (2017)
- [Overcoming information reduced data and experimentally uncertain parameters in ptychography with regularized optimization](https://mlsanity.com/paper/2005.01530/) (2020)
- [Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy](https://mlsanity.com/paper/2103.12165/) (2021)

## Built on by (most cited first, 3 of 3)

- [Neural Network Methods for Radiation Detectors and Imaging](https://mlsanity.com/paper/2311.05726/) (2023)
- [Machine learning for classifying and interpreting coherent X-ray speckle patterns](https://mlsanity.com/paper/2211.08194/) (2022)
- [Unsupervised Deep Image Prior for Sparse-View and Limited-Angle Electron Tomography](https://mlsanity.com/paper/2605.27139/) (2026)

## Abstract (arXiv)

We present a method that lowers the dose required for a ptychographic reconstruction by adaptively scanning the specimen, thereby providing the required spatial information redundancy in the regions of highest importance. The proposed method is built upon a deep learning model that is trained by reinforcement learning (RL), using prior knowledge of the specimen structure from training data sets. We show that equivalent low-dose experiments using adaptive scanning outperform conventional ptychography experiments in terms of reconstruction resolution.