Hierarchical Reinforcement Learning for Decision Support in Health Care
Bibliographic record
Abstract
Optimal decision-making is critical within many organizations. A large number of these organizations are structured hierarchically but make sequential decisions (like health care, for example). Using data collected over time by these organizations, we have been able to successfully apply reinforcement learning (RL) to many sequential decision-making problems. In doing this, however, we are not taking advantage of the benefits of their hierarchical structures and the ways different layers affect each other, and thus are not able to learn optimal decision-making techniques.Hierarchical reinforcement learning (HRL) is a powerful tool for solving extended problems with spare rewards. HRL decomposes a RL problem into a hierarchy of subtasks to be solved individually using RL. Because of this, the fundamental concept of HRL applies nicely to our problem. Unfortunately, due to their inability to handle multiple agents, implement batch learning, and model concurrent activities, classic HRL frameworks are not quite suitable for our problem. In our work, we plan to formalize a new HRL framework that is capable of building sequential decision-making support models using datasets collected from stochastic behavioral policies. In this paper, we bring light to probable obstacles as well as potential solutions to existing problems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 teacher head, 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".