An Adaptive Reinforcement Learning Approach to Policy-Driven Autonomic Management
Why this work is in the frame
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Bibliographic record
Abstract
Policies have been explored as a basis for autonomic management. In many cases, there is a need for policy-driven autonomic systems to have the ability to adapt the use of policies based, for example, on past experience, in order to deal with human error or the unpredictability in workload characteristics. This suggests that learning approaches can offer significant potential benefits in providing autonomic systems with the ability to identify preferred uses of existing policies or learn new policies. In this context, we have explored the use of reinforcement learning in adaptive policy-driven autonomic management. A key question is whether a model "learned'' from the use of one set of policies could be applied to another set of "similar'' policies, or whether a new model must be learned from scratch as a result of changes to an active set of policies. In this paper, we illustrate how a reinforcement learning model might be adapted to accommodate such changes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it