mlsanityData to 14 Oct 2026

Reinforcement Learning of Adaptive Acquisition Policies for Inverse Problems

arXiv 2407.07794 ↗First posted 10 Jul 2024cs.LG eess.SPcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

A promising way to mitigate the expensive process of obtaining a high-dimensional signal is to acquire a limited number of low-dimensional measurements and solve an under-determined inverse problem by utilizing the structural prior about the signal. In this paper, we focus on adaptive acquisition schemes to save further the number of measurements. To this end, we propose a reinforcement learning-based approach that sequentially collects measurements to better recover the underlying signal by acquiring fewer measurements. Our approach applies to general inverse problems with continuous action spaces and jointly learns the recovery algorithm. Using insights obtained from theoretical analysis, we also provide a probabilistic design for our methods using variational formulation. We evaluate our approach on multiple datasets and with two measurement spaces (Gaussian, Radon). Our results confirm the benefits of adaptive strategies in low-acquisition horizon settings.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 26 of its 48 references that are in the map. First posted on arXiv 10 Jul 2024.

Builds on

26 of 48 references.

References · 26

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2013Auto-Encoding Variational Bayescited by 4,053
  3. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  4. 2019Dream to Control: Learning Behaviors by Latent Imaginationcited by 1,653
  5. 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,304
  6. 2018Learning Latent Dynamics for Planning from Pixelscited by 1,264
  7. 2020Mastering Atari with Discrete World Modelscited by 1,007
  8. 2016RL2: Fast Reinforcement Learning via Slow Reinforcement Learningcited by 503
  9. 2016Learning to reinforcement learncited by 377
  10. 2015A Recurrent Latent Variable Model for Sequential Datacited by 362
  11. 2017Compressed Sensing using Generative Modelscited by 339
  12. 2021The Intrinsic Dimension of Images and Its Impact on Learningcited by 315
14 more
  1. 2019Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Modelcited by 231
  2. 2020PLAS: Latent Action Space for Offline Reinforcement Learningcited by 133
  3. 2018Deep Variational Reinforcement Learning for POMDPscited by 125
  4. 2018Temporal Difference Variational Auto-Encodercited by 49
  5. 2020Active MR k-space Sampling with Reinforcement Learningcited by 46
  6. 2020Experimental design for MRI by greedy policy searchcited by 40
  7. 2021LASER: Learning a Latent Action Space for Efficient Reinforcement Learningcited by 32
  8. 2019Self-Supervised Deep Active Accelerated MRIcited by 24
  9. 2022On learning adaptive acquisition policies for undersampled multi-coil MRI reconstructioncited by 14
  10. 2021End-to-End Sequential Sampling and Reconstruction for MRIcited by 13
  11. 2014Info-Greedy sequential adaptive compressed sensingcited by 9
  12. 2013Near-Optimal Adaptive Compressed Sensingcited by 8
  13. 2019Latent Space Reinforcement Learning for Steering Angle Predictioncited by 2
  14. 2023HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI Reconstructioncited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Adaptive sampling using variational autoencoder and reinforcement learning
  2. 2025Solving Diffusion Inverse Problems with Restart Posterior Sampling
  3. 2026Learning To Sample From Diffusion Models Via Inverse Reinforcement Learning
  4. 2025A Recovery Theory for Diffusion Priors: Deterministic Analysis of the Implicit Prior Algorithm
  5. 2026Triadic Dynamics Aware Diffusion Posterior Sampling for Inverse Problems: Optimizing Guidance and Stochasticity Schedules
  6. 2025Solving Bayesian inverse problems with diffusion priors and off-policy RL
  7. 2024Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint
  8. 2022Environment Design for Inverse Reinforcement Learning