Inductive biases and generalisation for deep reinforcement learning - Igl, M (2021)
In this thesis we aim to improve generalisation in deep reinforcement learning. Generalisation is a fundamental challenge for any type of learning, determining how acquired knowledge can be transferred to new, previously unseen situations. We focus on reinforcement learning, a framework describing how artificial agents can learn to interact with their environment to achieve goals. In recent years, by using neural networks to represent agents, it has achieved remarkable success and vastly expanded its scope of possible applications. Our goal is to improve the performance of these agents by allowing them to learn faster, to learn better solutions and to react robustly to previously unseen situations. On this quest, we explore a range of different methods and approaches.