Learning Closed-Loop Control Policies for Manufacturing

A manufacturing system and method involves learning a self-correcting closed-loop control policy through machine reinforcement learning for a manufacturing process that involves on-the-fly adjustment of process parameters to handle inconsistencies in the manufacturing process and material formulations, and controlling operation of a tool configured to interact with or produce a product including dynamically adjusting at least one parameter of the manufacturing process to thereby dynamically adjust operation of the tool based on qualitative performance information derived from at least one sensor applied as feedback to the closed-loop control policy learned through machine reinforcement learning.