Hierarchical Imitation and Reinforcement Learning

Understand

We study how to effectively leverage expert feedback to learn sequential decision-making policies.

• We focus on problems with sparse rewards and long time horizons, which typically pose significant challenges in reinforcement learning.
• We propose an algorithmic framework, called hierarchical guidance, that leverages the hierarchical structure of the underlying problem to integrate different modes of expert interaction.
• Our framework can incorporate different combinations of imitation learning (IL) and reinforcement learning (RL) at different levels, leading to dramatic reductions in both expert effort and cost of exploration.