Transfer in sequential decision making - Luketina, J (2023)
Transfer learning is critical for improving the data efficiency and applicability of deep learning models in sequential decision-making. However, determining what knowledge transfers and how to effectively leverage it remains an open challenge. Recent breakthroughs in representation learning, especially in language and vision domains, demonstrate the power of transfer from large-scale datasets. Meanwhile, progress in simulation platforms and environment designs has opened up new possibilities for collecting diverse, realistic training data. Against this backdrop, the four works contained in this thesis explore transfer techniques in various aspects of sequential decision-making.