From Restless to Contextual: A Thresholding Bandit Approach to Improve Finite-horizon Performance

• URL: http://arxiv.org/abs/2502.05145v1
• Date: Fri, 07 Feb 2025 18:23:43 GMT
• Title: From Restless to Contextual: A Thresholding Bandit Approach to Improve Finite-horizon Performance
• Authors: Jiamin Xu, Ivan Nazarov, Aditya Rastogi, África Periáñez, Kyra Gan,
• Abstract summary: Online restless bandits extend classic contextual bandits by incorporating state transitions and budget constraints. We reformulate the problem as a scalable budgeted thresholding contextual bandit problem. We propose an algorithm that achieves minimax optimal constant regret in the online multi-state setting.
• Score: 4.770896774729555
• License:
• Abstract: Online restless bandits extend classic contextual bandits by incorporating state transitions and budget constraints, representing each agent as a Markov Decision Process (MDP). This framework is crucial for finite-horizon strategic resource allocation, optimizing limited costly interventions for long-term benefits. However, learning the underlying MDP for each agent poses a major challenge in finite-horizon settings. To facilitate learning, we reformulate the problem as a scalable budgeted thresholding contextual bandit problem, carefully integrating the state transitions into the reward design and focusing on identifying agents with action benefits exceeding a threshold. We establish the optimality of an oracle greedy solution in a simple two-state setting, and propose an algorithm that achieves minimax optimal constant regret in the online multi-state setting with heterogeneous agents and knowledge of outcomes under no intervention. We numerically show that our algorithm outperforms existing online restless bandit methods, offering significant improvements in finite-horizon performance.

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