## 1 Introduction  
Manufacturing and logistic systems play a pivotal role in companies, serving as the backbone of their operations. Effective management of manufacturing and logistic systems is crucial for meeting customer demand, reducing costs, improving operational efficiency, and gaining a competitive advantage in the market. In today's ever-evolving landscape of manufacturing and logistic systems, characterized by increasing complexity and interdependencies, simulation has emerged as a powerful and versatile tool that can significantly aid managerial decision-making [1]. Simulation serves as a powerful tool for accurately modeling and analyzing intricate and dynamic systems that exhibit non-linear interactions. Among various simulation approaches, discrete-event simulation (DES) is widely adopted across multiple industries. A plethora of commercial simulators such as Simio, AnyLogic, FlexSim, and others, as well as non-commercial ones like SimPy and Salabim, provide robust capabilities for DES modeling. The potential of these simulators can be greatly amplified by integrating them with advanced optimization or machine learning tools. Connecting DES platforms with external programming languages such as Python or R can enhance simulation modeling by leveraging additional mathematical and algorithmic capabilities, thereby enabling more sophisticated analyses and insights [2]. Reinforcement learning (RL) [3] is an increasingly popular field of machine learning in artificial intelligence, which studies how intelligent agents ought to take actions in an environment with the aim of maximizing the cumulative reward. RL has plenty of applications in a wide range of fields such as natural language processing, image processing, recommendation systems, and marketing, among others. In the context of warehouse operations, RL can be used for various applications to optimize and automate tasks, improve efficiency, and reduce costs [4]. Some examples of RL applications in this context include inventory management (decisions on when to reorder, how much to reorder, and where to store items), order picking optimization (decisions on the most efficient routes for order picking), warehouse layout optimization (decisions on the arrangement of aisles, racks, and storage areas), autonomous guided vehicles routing, energy management (decisions on energy usage, such as scheduling equipment operations, adjusting temperature setpoints, and optimizing lighting), and quality control (decisions on item inspection based on factors such as item characteristics, inspection history, and inspection costs). Combining advanced simulation environments and RL can be used for training an agent to find efficient policies that take into account realistic conditions such as those present in a warehouse. In this context, our work explores the potential of combining the well-established commercial simulation software FlexSim [5] and various RL implementations, programmed in Python [6]. FlexSim has several advantages that make it suitable for this project: _(i)_ the focus on warehouse and manufacturing applications; _(ii)_ the visual aspect, which facilitates the validation and also boosts managerial decision-making and insights by graphically showing the content of the model and the progress of the simulation; and _(iii)_ the option to communicate with Python [7]. On the other hand, Python stands out as an open source, platform-independent, and general-purpose programming language. It offers plenty of comprehensive packages for data analysis, machine learning, scientific computing, and application development, among others. We present a case study where a dynamic version of the storage location assignment problem [8] is addressed. It illustrates the benefits and limitations of this approach and boosts a discussion on its potential in warehouse operations.