[2603.26816] PiCSRL: Physics-Informed Contextual Spectral Reinforcement Learning

Computer Science > Machine Learning arXiv:2603.26816 (cs) [Submitted on 26 Mar 2026] Title:PiCSRL: Physics-Informed Contextual Spectral Reinforcement Learning Authors:Mitra Nasr Azadani, Syed Usama Imtiaz, Nasrin Alamdari View a PDF of the paper titled PiCSRL: Physics-Informed Contextual Spectral Reinforcement Learning, by Mitra Nasr Azadani and 1 other authors View PDF HTML (experimental) Abstract: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...