Curriculum Learning in Reinforcement Learning

Curriculum Learning in Reinforcement Learning

Sanmit Narvekar

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Doctoral Consortium. Pages 5195-5196. https://doi.org/10.24963/ijcai.2017/757

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.
Keywords:
Artificial Intelligence: artificial intelligence
Artificial Intelligence: machine learning
Artificial Intelligence: agents and multi-agent systems