Adaptive Scheduling: A Reinforcement Learning Whittle Index Approach for Wireless Sensor Networks
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arXiv
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| Format: | Preprint |
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2026
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| author | Jonah, Sokipriala Yoo, Seong Ki Sthapit, Saurav |
| author_facet | Jonah, Sokipriala Yoo, Seong Ki Sthapit, Saurav |
| contents | We propose a reinforcement learning based scheduling framework for Restless Multi-Armed Bandit (RMAB) problems, centred on a Whittle Index Q-Learning policy with Upper Confidence Bound (UCB) exploration, referred to as WIQL-UCB. Unlike existing approaches that rely on fixed or adaptive epsilon-greedy strategies and require careful hyperparameter tuning, the proposed method removes problem-specific tuning and is therefore more generalisable across diverse RMAB settings. We evaluate WIQL-UCB on standard RMAB benchmarks and on a practical sensor scheduling application based on the Age of Incorrect Information (AoII), using an edge-based state estimation scheme that requires no prior knowledge of system dynamics. Experimental results show that WIQL-UCB achieves near-optimal performance while significantly improving computational and memory efficiency. For a representative problem size of N = 15 and M = 3, the proposed method requires only around 600 bytes of memory, compared with several kilobytes for tabular Q-learning and hundreds of kilobytes to megabytes for deep reinforcement learning baselines. In addition, WIQL-UCB achieves sub-millisecond per-decision runtimes and is several times faster than deep reinforcement learning approaches, while maintaining competitive performance. Overall, these results demonstrate that WIQL-UCB consistently outperforms both non-Whittle-based and Whittle-index learning baselines across a wide range of RMAB settings. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_01179 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adaptive Scheduling: A Reinforcement Learning Whittle Index Approach for Wireless Sensor Networks Jonah, Sokipriala Yoo, Seong Ki Sthapit, Saurav Systems and Control We propose a reinforcement learning based scheduling framework for Restless Multi-Armed Bandit (RMAB) problems, centred on a Whittle Index Q-Learning policy with Upper Confidence Bound (UCB) exploration, referred to as WIQL-UCB. Unlike existing approaches that rely on fixed or adaptive epsilon-greedy strategies and require careful hyperparameter tuning, the proposed method removes problem-specific tuning and is therefore more generalisable across diverse RMAB settings. We evaluate WIQL-UCB on standard RMAB benchmarks and on a practical sensor scheduling application based on the Age of Incorrect Information (AoII), using an edge-based state estimation scheme that requires no prior knowledge of system dynamics. Experimental results show that WIQL-UCB achieves near-optimal performance while significantly improving computational and memory efficiency. For a representative problem size of N = 15 and M = 3, the proposed method requires only around 600 bytes of memory, compared with several kilobytes for tabular Q-learning and hundreds of kilobytes to megabytes for deep reinforcement learning baselines. In addition, WIQL-UCB achieves sub-millisecond per-decision runtimes and is several times faster than deep reinforcement learning approaches, while maintaining competitive performance. Overall, these results demonstrate that WIQL-UCB consistently outperforms both non-Whittle-based and Whittle-index learning baselines across a wide range of RMAB settings. |
| title | Adaptive Scheduling: A Reinforcement Learning Whittle Index Approach for Wireless Sensor Networks |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2601.01179 |