Index Policy

Index policies are heuristic algorithms designed to efficiently solve restless multi-armed bandit problems (RMABs), a class of sequential decision-making problems where the state of each option changes even when not selected. Current research focuses on developing and analyzing improved index policies, particularly those based on reinforcement learning (e.g., Q-learning, deep learning) and Whittle index approximations, aiming to overcome the computational challenges posed by large state spaces and non-separable rewards. These advancements have significant implications for resource allocation in diverse fields, including wireless communication, healthcare, and task scheduling, by providing near-optimal solutions to complex optimization problems.