Concept and the implementation of a tool to convert industry 4.0 environments modeled as FSM to an OpenAI Gym wrapper
This provides a practical solution for applying RL in Industry 4.0 optimization tasks, though it is incremental as it builds on existing frameworks.
The paper tackles the lack of industrial environments for reinforcement learning by presenting a tool that converts systems modeled as finite state machines into OpenAI Gym wrappers, enabling the application of RL methods; initial tests demonstrate Q-learning and Deep Q-learning on simple environments.
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.