Digitization fostered by the Industry 4.0 paradigm and smart factories leads to more connectivity and data abundance but also to a more dynamic industrial environment that makes scheduling an even harder problem. Large factories with complex configurations like Hybrid Flow-Shops (HFS) cannot rely on centralized, reactive, and non-adaptive heuristics, or metaheuristics that produce high-quality schedules but are time-expensive. We propose MARTES, a Multi-Agent Reinforcement Learning (MARL) Training Environment for Scheduling. In this work, MARTES trains models to be used in HFS scenarios. The resulting models decide among different dispatching rules to select what job to process next. The results show that exploiting MARTES models yields high-quality schedulings, outperforming traditional dispatching rules like First Come First Serve, Earliest Deadline First, or Shortest Job First by even a 26.4%, increasing the deadlines met by jobs in more than 30%, and improving tardiness by even a 50.5% in time-constrained scenarios. MARTES models can also compete in performance with heuristics as NEH, and metaheuristics as genetic (GA) or iterative greedy (IG) algorithms, differing in less than a 1% in makespan results for large instances. Time-wise, NEH can be up to 2 orders of magnitude slower than MARTES models' training times. GA or IG execution times can be similar to MARTES models' training times but require additional executions when changes occur on the factory pipeline, unlike MARTES models.