A Reinforcement Learning Framework for Efficient Informative Sensing
Bibliographic record
Abstract
Large-scale spatial data can be collected using mobile robots with sensing and navigation capabilities. Due to limited battery lifetime and scarcity of charging stations, it is important to plan informative paths so as to maximize the utility of data given a limited travel budget, which is known as the informative path planning (IPP) problem. IPP is NP-hard, and existing solutions suffer from high complexity or low optimality. In this paper, we present a novel IPP solution based on reinforcement learning (RL). The basic idea is to learn the structural characteristics of informative paths, so informative paths can be predicted. As such, when budgets change, we avoid solving the problem from scratch and thus path planning efficiency can be improved dramatically. Among the 20 path planning experiments in two areas, the proposed RL based solution achieves the best path utility in 15 experiments, compared with state-of-the-art algorithms. More importantly, the inference complexity is linear with respect to the budget (equivalently, the maximum number of steps in RL), which is lower than other solutions. Despite the NP-hardness, the path planning process can be finished within a few seconds in our experiments on two graphs of different sizes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".