@inproceedings{zhang2024whittle,
author = {Zhang, Chuheng and Wang, Xiangsen and Jiang, Wei and Yang, Xianliang and Wang, Siwei and Song, Lei and Bian, Jiang},
title = {Whittle Index with Multiple Actions and State Constraint for Inventory Management},
booktitle = {2024 International Conference on Learning Representations},
year = {2024},
month = {May},
abstract = {Whittle index is a heuristic tool that leads to good performance for the restless bandits problem. In this paper, we extend Whittle index to a new multi-agent reinforcement learning (MARL) setting with multiple discrete actions and a possibly changing constraint on the state space, resulting in WIMS (Whittle Index with Multiple actions and State constraint). This setting is common for inventory management where each agent chooses a replenishing quantity level for the corresponding stock-keeping-unit (SKU) such that the total profit is maximized while the total inventory does not exceed a certain limit. Accordingly, we propose a deep MARL algorithm based on WIMS for inventory management. Empirically, our algorithm is evaluated on real large-scale inventory management problems with up to 2307 SKUs and outperforms operation-research-based methods and baseline MARL algorithms.},
url = {http://approjects.co.za/?big=en-us/research/publication/whittle-index-with-multiple-actions-and-state-constraint-for-inventory-management/},
}