… optimization and risk sensitive formulations for distributional RL. Hybridization of derivative-…
via distributional RL. Finally, we use a multi-echelon supply chain inventory management …

… We consider inventory management for a multi-echelon, multi-period supply chain: the
goal is to maximize profits while satisfying constraints. We use InvManagement-v0 in the or-gym …

… and distributional ambiguity. Distributionally robust optimization has also been exploited for
tactical healthcare supply chain management … the assumption of distributional ambiguity and …

… Reinforcement learning (RL) is a promising solution for difficult decision-making problems,
such as inventory management in chemical supply chains. However, enabling RL to explicitly …

… This chapter begins with a review of inventory management policies, focusing on … , inventory
review frequency, and push versus pull strategies. It explores classic inventory management …

… our inventory management problem. Section 4 discusses the methodology of the proposed
structured RL algorithm for inventory management. … under this distributional assumption for …

… Second, we introduce GTDQN, a distributional reinforcement … It outperforms other distributional
reinforcement learning … Inventory management considers a multitude of factors. One …

… of distributional RL algorithms [32]. Instead of formalizing the objective via expected
performance, distributional RL … In this work, we utilize distributional RL to consider risk-sensitive …

… distributional information for all sources of uncertainty can lead to substantial cost reductions
in inventory management … that the importance of including distributional information tends to …

… Most solutions to the inventory management problem assume a centralization of information
… A decentralized solution to inventory management using multi-agent reinforcement learning …