Constrained Restless Bandits for Dynamic Scheduling in Cyber-Physical Systems
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".