Exploring Intrinsically Motivated Feedback Mechanisms in Reinforcement Learning for Inventory Management

Abstract

The field of inventory management has become increasingly complex, necessitating sophisticated decision-making models that can adapt to dynamic environments. Reinforcement Learning (RL) has emerged as a powerful tool for addressing these challenges by enabling agents to learn optimal policies through interactions with their environment. However, traditional RL approaches often struggle with sparse feedback and delayed rewards, which can hinder their effectiveness in practical applications like inventory management. This paper explores the concept of intrinsically motivated feedback mechanisms in RL, emphasizing their role in enhancing learning efficiency and adaptability. By investigating various intrinsic motivation strategies, we aim to improve inventory management systems, making them more responsive to fluctuations in demand and supply. Through experimental simulations, we illustrate the efficacy of these methods, demonstrating their potential to transform inventory management practices in real-world scenarios.