Reinforcement learning has generated human-level decision-making strategies in highly complex game scenarios. But most industries, such as manufacturing, have not seen impressive results from the application of these algorithms, belying the utility hoped for by their creators. The limitations of reinforcement learning in real use cases intuitively manifest from the number of exploration examples needed to train the underlying models, but also from incomplete state representations for an artificial agent to act on. In an effort to improve automated inspection for factory control through reinforcement learning, our research is focused on improving the state representation of a manufacturing process using optical inspection as a basis for agent optimization. In this presentation, we focus on the imaging system: its design, implementation and utilization, in the context of a reinforcement agent.