Outcome-Fair Restless Multi-Armed Bandits for Stochastic Deadline Scheduling
We study a restless multi-armed bandit (RMAB) problem for a stochastic deadline scheduling application. RMAB problems are solved using the Whittle index policy. The goal in RMAB is to maximize the expected cumulative discounted reward maximization. The Whittle index policy maximizes reward, but is not fair among two classes. In this paper, we introduce fairness criteria and study an outcome-fair model for RMAB which allows fairness for jobs and users structurally disadvantaged demographic classes. We formulate an outcome fair stochastic deadline scheduling problem as RMAB, and we develop the outcome fair Whittle index policy. We define a virtual queue mechanism that dynamically enforces l...
| Authors | Shakti Sharma, Rahul Meshram |
|---|---|
| Primary category | eess.SY |
| Published | 26 July 2026 |
| Last updated at source | 26 July 2026 |
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