Part 1 explores the ability of a model trained with reinforcement learning (RL) to generalize, i.e., produce acceptable results when presented with data it was not exposed to during training. The application in this study is an industrial process with multiple controls that determine the effect on a product as it transitions through the process. […]
Training With a Digital Twin Part 1 described a reinforcement learning system used to find the optimal control settings for a reflow oven used for soldering electronic components to a circuit board. Part 2 presents the details of the oven simulator used to accelerate the training process. Continue reading…
A previous post describes a reinforcement learning model trained to find the optimal control settings for a reflow oven that solders electronic components to a circuit board. The oven’s moving belt transports the product (i.e., the circuit board) through multiple heating zones. This process heats the product according to a temperature-time target profile required to […]
Can Reinforcement Learning generalize beyond its training? This paper explores the ability of a model trained with reinforcement learning (RL) to generalize, i.e., produce acceptable results when presented with data it was not exposed to during training. The application in this study is an industrial process with multiple controls that determine the effect on a […]
Tuning a Process Oven with Reinforcement Learning Determining optimal control settings for an industrial process can be tough. For instance, controls can interact, where adjusting one setting requires readjustment of other settings. Also, the relationship between a control and its effect can be very complex. Such complications can be challenging for optimizing a process. This […]
