Curriculum Learning in Reinforcement Learning

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 an approach for modeling transferability between tasks, and methods for semi-automatically generating source tasks tailored to an agent and the characteristics of a target domain. Finally, we also present ideas for future work.