Deep reinforcement learning for industrial applications

In recent years there has been a growing attention from the world of research and companies in the field of Machine Learning. This interest, thanks mainly to the increasing availability of large amounts of data, and the respective strengthening of the hardware sector useful for their analysis, has led to the birth of Deep Learning. The growing computing capacity and the use of mathematical optimization techniques, already studied in depth but with few applications due to a low computational power, have then allowed the development of a new approach called Reinforcement Learning. This thesis work is part of an industrial process of selection of fruit for sale, thanks to the identification and classification of any defects present on it. The final objective is to measure the similarity between them, being able to identify and link them together, even if coming from optical acquisitions obtained at different time steps. We therefore studied a class of algorithms characteristic of Reinforcement Learning, the policy gradient methods, in order to train a feedforward neural network to compare possible malformations of the same fruit. Finally, an applicability study was made, based on real data, in which the model was compared on different fruit rolling dynamics and with different versions of the network.