ContextWIN: Whittle Index Based Mixture-of-Experts Neural Model For Restless Bandits Via Deep RL
This work addresses complex decision-making scenarios in dynamic environments, such as recommendation systems, but is incremental as it builds on the existing NeurWIN model.
The paper tackles Restless Multi-Armed Bandit problems by introducing ContextWIN, a context-aware neural model that integrates a mixture of experts with reinforcement learning to improve decision-making in dynamic environments like recommendation systems, achieving enhanced efficiency and accuracy in Whittle index computation.
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.