# PiCSRL: Physics-Informed Contextual Spectral Reinforcement Learning

arXiv: https://arxiv.org/abs/2603.26816 · HTML: https://mlsanity.com/paper/2603.26816/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 6 of 6)

- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Learning to Optimize via Information-Directed Sampling](https://mlsanity.com/paper/1403.5556/) (2014)
- [Recent Advances in Domain Adaptation for the Classification of Remote Sensing Data](https://mlsanity.com/paper/2104.07778/) (2021)
- [Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder](https://mlsanity.com/paper/2105.00026/) (2021)
- [SpecTM: Spectral Targeted Masking for Trustworthy Foundation Models](https://mlsanity.com/paper/2603.22097/) (2026)
- [High Resolution Flood Extent Detection Using Deep Learning with Random Forest Derived Training Labels](https://mlsanity.com/paper/2603.22518/) (2026)

## Built on by (most cited first, 1 of 1)

- [Scalable and Trustworthy Earth Observation Foundation Models](https://mlsanity.com/paper/2607.07758/) (2026)

## Abstract (arXiv)

High-dimensional low-sample-size (HDLSS) datasets constrain reliable environmental model development, where labeled data remain sparse. Reinforcement learning (RL)-based adaptive sensing methods can learn optimal sampling policies, yet their application is severely limited in HDLSS contexts. In this work, we present PiCSRL (Physics-Informed Contextual Spectral Reinforcement Learning), where embeddings are designed using domain knowledge and parsed directly into the RL state representation for improved adaptive sensing. We developed an uncertainty-aware belief model that encodes physics-informed features to improve prediction. As a representative example, we evaluated our approach for cyanobacterial gene concentration adaptive sampling task using NASA PACE hyperspectral imagery over Lake Erie. PiCSRL achieves optimal station selection (RMSE = 0.153, 98.4% bloom detection rate, outperforming random (0.296) and UCB (0.178) RMSE baselines, respectively. Our ablation experiments demonstrate that physics-informed features improve test generalization (0.52 R^2, +0.11 over raw bands) in semi-supervised learning. In addition, our scalability test shows that PiCSRL scales effectively to large networks (50 stations, >2M combinations) with significant improvements over baselines (p = 0.002). We posit PiCSRL as a sample-efficient adaptive sensing method across Earth observation domains for improved observation-to-target mapping.