  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.
