Industry 4.0 systems have a high demand for optimization in their tasks, whether to minimize cost, maximize production, or even synchronize their actuators to finish or speed up the manufacture of a product. Those challenges make industrial environments a suitable scenario to apply all modern reinforcement learning (RL) concepts. The main difficulty, however, is the lack of that industrial environments. In this way, this work presents the concept and the implementation of a tool that allows us to convert any dynamic system modeled as an FSM to the open-source Gym wrapper. After that, it is possible to employ any RL methods to optimize any desired task. In the first tests of the proposed tool, we show traditional Q-learning and Deep Q-learning methods running over two simple environments.

中文翻译：

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将建模为 FSM 的工业 4.0 环境转换为 OpenAI Gym 包装器的工具的概念和实现

工业 4.0 系统对其任务的优化要求很高，无论是最小化成本、最大化产量，还是同步执行器以完成或加快产品制造。这些挑战使工业环境成为应用所有现代强化学习 (RL) 概念的合适场景。然而，主要的困难是缺乏那种工业环境。通过这种方式，这项工作提出了一个工具的概念和实现，该工具允许我们将任何建模为 FSM 的动态系统转换为开源 Gym 包装器。之后，可以使用任何 RL 方法来优化任何所需的任务。在建议工具的第一次测试中，我们展示了在两个简单环境中运行的传统 Q-learning 和 Deep Q-learning 方法。