… This paper provides a roadmap on how inventory control may benefit from DRL. The two …
reinforcement learning. Deep reinforcement learning refers to the use of neural networks in …

… Reinforcement learning (RL) appeals to many researchers in recent years because of its …
a case-based reinforcement learning algorithm (CRL) for dynamic inventory control in a multi-…

… The outperformance of deep reinforcement learning has also been shown by Ke et al. [6] …
up deep reinforcement learning models to study the joint pricing and inventory control problem …

… control problem for these enterprises, this paper proposes use of Deep Reinforcement
Learning (RL) techniques for the inventory … The paper models the retailers inventory limitation …

… Modeling inventory control problem through SMDPs rather than MDPs presents several … it
is shown how reinforcement learning can be used to address supply chain inventory problems. …

… a generic dynamical system control problem and show that multi-product inventory management
is a special case. We also develop the equivalent reinforcement learning formulation, …

… of machine learning in inventory optimization. The … reinforcement learning (DRL) agent to
play the beer game, a popular classroom activity that demonstrates certain aspects of inventory …

… topic is hotter today than machine learning and artificial intelligence. We focus on deep
reinforcement learning (DRL), the subfield of machine learning that develops “prescriptions” or …

… probability, classical reinforcement learning algorithms usually … a Flexible Fuzzy Reinforcement
Learning algorithm, in which … of the system are tuned during the learning phase. Next, the …

… Case-based reinforcement learning for dynamic inventory control in a multi-agent supply-chain
system. Expert Systems with Applications 36, 3 (2009), 6520–6526. [21] Kaggle. …