Whittle Index Learning Algorithms for Restless Bandits with Constant Stepsizes
<|reference_start|>Whittle Index Learning Algorithms for Restless Bandits with Constant Stepsizes: We study the Whittle index learning algorithm for restless multi-armed bandits. We consider index learning algorithm with Q-learning. We first present Q-learning algorithm with exploration policies -- epsilon-greedy, softmax, epsilon-softmax with constant stepsizes. We extend the study of Q-learning to index learning for single-armed restless bandit. The algorithm of index learning is two-timescale variant of stochastic approximation, on slower timescale we update index learning scheme and on faster timescale we update Q-learning assuming fixed index value. In Q-learning updates are in asynchronous manner. We study constant stepsizes two timescale stochastic approximation algorithm. We provide analysis of two-timescale stochastic approximation for index learning with constant stepsizes. Further, we present study on index learning with deep Q-network (DQN) learning and linear function approximation with state-aggregation method. We describe the performance of our algorithms using numerical examples. We have shown that index learning with Q learning, DQN and function approximations learns the Whittle index.<|reference_end|>
@article{mittal2024whittle,
    title={Whittle Index Learning Algorithms for Restless Bandits with Constant
  Stepsizes},
    author={Vishesh Mittal, Rahul Meshram and Surya Prakash},
    journal={arXiv preprint arXiv:2409.04605},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.04605},
    primaryClass={cs.LG cs.SY eess.SY stat.ML}
}