Robust transfer learning in multi-robot systems by using sparse autoencoder

Robust algorithms for transfer learning in multirobot systems based on elements of the deep learning are proposed in the paper. The algorithms are based on using the sparse autoencoder. The main ideas underlying the algorithms are to extend the set of set-valued observations by training examples having uncertain weights and to apply the robust minimax strategy in order to find an optimal autoencoder for dealing with set-valued observations. An interesting scheme for transfer learning is considered for which source learning set is reconstructed by means of the sparse autoencoder trained on the target learning set.