Coupling Semi-supervised Learning with Reinforcement Learning for Better Decision Making -- An application to Cryo-EM Data CollectionDownload PDF

22 Sept 2022, 12:32 (modified: 26 Oct 2022, 13:59)ICLR 2023 Conference Withdrawn SubmissionReaders: Everyone
Keywords: Reinforcement Learning, Semi-supervised Learning, Cryo-EM
TL;DR: We proposed an iterative semi-supervised learning framework for dual-learning of RL and the perception model with applications to Cryo-EM.
Abstract: We consider a semi-supervised Reinforcement Learning (RL) approach that takes inputs from a perception model. Performance of such an approach can be significantly limited by the quality of the perception model in the low labeled data regime. We propose a novel iterative framework that simultaneously couples and improves the training of both RL and the perception model. The perception model takes pseudo labels generated from the trajectories of a trained RL agent believing that the decision-model can correct errors made by the perception model. We apply the framework to cryo-electron microscopy (cryo-EM) data collection, whose goal is to find as many high-quality micrographs taken by cryo-electron microscopy as possible by navigating at different magnification levels. Our proposed method significantly outperforms various baseline methods in terms of both RL rewards and the accuracy of the perception model. We further provide some theoretical insights into the benefits of coupling the decision model and the perception model by showing that RL-generated pseudo labels are biased towards localization which aligns with the underlying data generating mechanism. Our iterative framework that couples both sides of the semi-supervised RL can be applied to a wide range of sequential decision-making tasks when the labeled data is limited.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
Submission Guidelines: Yes
Please Choose The Closest Area That Your Submission Falls Into: Machine Learning for Sciences (eg biology, physics, health sciences, social sciences, climate/sustainability )
Supplementary Material: zip
4 Replies