Constrained Restless Bandits for Dynamic Scheduling in Cyber-Physical Systems
Why this work is in the frame
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Bibliographic record
Abstract
This paper develops a sequential decision-making framework called constrained restless multi-armed bandits (CRMABs) to model problems of resource allocation under uncertainty and dynamic availability constraints. The decision-maker’s objective is to maximize the long-term cumulative reward. This can only be achieved by considering the impact of current actions on the future evolution of states. The uncertainty about the future availability of arms and partial state-information makes this objective challenging. CRMABs can be applied to resource allocation problems in cyber-physical systems, including sensor/relay scheduling. Whittle’s index policy, online rollout, and myopic policies are studied as solutions for CRMABs. First, the conditions for the applicability of Whittle’s index policy are studied, and the indexability result is claimed under some structural assumptions. An algorithm for index computation is presented. The online rollout policy for partially observable CRMABs is proposed as a low-complexity alternative to the index policy, and the complexity of these schemes is analyzed. An upper bound on the optimal value function is derived, which helps assess the sub-optimality of various solutions. The simulation study compares the performance of these policies and shows that the rollout policy is the better performing solution. In some settings it shows about 14% gain relative to Whittle’s index and myopic policies. Finally, an application to the problem of wildfire management is presented. Decision-making using CRMABs is analyzed from the perspective of a central agency tasked with fighting wildfires in multiple regions under logistic constraints.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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