Finding the best design parameters for optical nanostructures using reinforcement learning
This provides a method for improving optical nanostructure design, with potential extensions to other optical structures, but it is incremental as it adapts an existing reinforcement learning technique to a new domain.
The paper tackled the problem of optimizing color generation in dielectric nanostructures by applying deep Q-learning, achieving deeper red, green, and blue colors compared to human-designed parameters.
Recently, a novel machine learning model has emerged in the field of reinforcement learning known as deep Q-learning. This model is capable of finding the best possible solution in systems consisting of millions of choices, without ever experiencing it before, and has been used to beat the best human minds at complex games such as, Go and chess, which both have a huge number of possible decisions and outcomes for each move. With a human-level intelligence, it has been solved the problems that no other machine learning model could do before. Here, we show the steps needed for implementing this model on an optical problem. We investigated the colour generation by dielectric nanostructures and show that this model can find geometrical properties that can generate a much deeper red, green and blue colours compared to the ones found by human researchers. This technique can easily be extended to predict and find the best design parameters for other optical structures.