The integration of Model Predictive Control and deep Reinforcement Learning for efficient thermal control in thermoforming processes

This paper presents an approach to integrate Model Predictive Control (MPC) and deep Reinforcement Learning (RL) to improve the efficiency in radiation thermal control systems, specifically in the heating phase of the thermoforming process where a considerable number of radiation heating elements are used as actuators. Because of the large action and state spaces in such systems, the exploration process during the agent training takes a long time. The strategy in this paper employs an MPC to guide and expedite the training process of deep RL agents. While MPC performs optimally with well-defined models and can handle constraints, it requires that model parameters stay constant over time and its online computational burden is notable, especially in systems with extensive action and state spaces. The Proposed approach leverages the predictive capabilities of MPC to provide an external action input that can guide the deep RL agent's exploration and learning process. Hence, at the end of the training process, the trained agent will perform close to optimally while its online computational burden is very low compared to MPC. In our designed heating system, this hybrid method dramatically accelerates learning, achieving a remarkable 100-fold increase in average episode rewards during training compared to traditional deep RL techniques. Furthermore, the trained agent is not only robust to environmental disturbances, but its online computing burden is 14 times lower than that of MPC. This approach stands as a promising solution for efficient and effective thermal control in industrial applications.