Abstract
Transfer learning in reinforcement learning is an
area of research that seeks to speed up or improve
learning of a complex target task, by leveraging
knowledge from one or more source tasks. This
thesis will extend the concept of transfer learning
to curriculum learning, where the goal is to design
a sequence of source tasks for an agent to train on,
such that final performance or learning speed is improved. We discuss completed work on this topic,
including methods for semi-automatically generating source tasks tailored to an agent and the characteristics of a target domain, and automatically sequencing such tasks into a curriculum. Finally, we
also present ideas for future work