This study introduces ContextWIN, a novel architecture that extends the Neural Whittle Index Network (NeurWIN) model to address Restless Multi-Armed Bandit (RMAB) problems with a context-aware approach. By integrating a mixture of experts within a reinforcement learning framework, ContextWIN adeptly utilizes contextual information to inform decision-making in dynamic environments, particularly in recommendation systems. A key innovation is the model's ability to assign context-specific weights to a subset of NeurWIN networks, thus enhancing the efficiency and accuracy of the Whittle index computation for each arm. The paper presents a thorough exploration of ContextWIN, from its conceptual foundation to its implementation and potential applications. We delve into the complexities of RMABs and the significance of incorporating context, highlighting how ContextWIN effectively harnesses these elements. The convergence of both the NeurWIN and ContextWIN models is rigorously proven, ensuring theoretical robustness. This work lays the groundwork for future advancements in applying contextual information to complex decision-making scenarios, recognizing the need for comprehensive dataset exploration and environment development for full potential realization.

翻译：本研究提出了一种新颖的架构ContextWIN，它扩展了神经Whittle索引网络（NeurWIN）模型，以采用上下文感知的方法解决不安定多臂赌博机（RMAB）问题。通过将专家混合集成到强化学习框架中，ContextWIN能够巧妙地利用上下文信息来指导动态环境中的决策，特别是在推荐系统中。一个关键的创新是该模型能够为NeurWIN网络的一个子集分配特定于上下文的权重，从而提高了每个臂的Whittle索引计算的效率和准确性。本文对ContextWIN进行了全面的探讨，从其概念基础到实现和潜在应用。我们深入研究了RMAB的复杂性以及纳入上下文的重要性，重点阐述了ContextWIN如何有效地利用这些要素。NeurWIN和ContextWIN模型的收敛性都得到了严格证明，确保了理论的稳健性。这项工作为未来在复杂决策场景中应用上下文信息的进展奠定了基础，同时认识到需要全面的数据集探索和环境开发以实现其全部潜力。