Few-Shot Intent Inference via Meta-Inverse Reinforcement Learning

Kelvin Xu, Ellis Ratner, Anca Dragan, Sergey Levine, Chelsea Finn

Abstract: A significant challenge for the practical application of reinforcement learning toreal world problems is the need to specify an oracle reward function that correctly defines a task. Inverse reinforcement learning (IRL) seeks to avoid this challenge by instead inferring a reward function from expert behavior. While appealing, it can be impractically expensive to collect datasets of demonstrations that cover the variation common in the real world (e.g. opening any type of door). Thus in practice, IRL must commonly be performed with only a limited set of demonstrations where it can be exceedingly difficult to unambiguously recover a reward function. In this work, we exploit the insight that demonstrations from other tasks can be used to constrain the set of possible reward functions by learning a "prior" that is specifically optimized for the ability to infer expressive reward functions from limited numbers of demonstrations. We demonstrate that our method can efficiently recover rewards from images for novel tasks and provide intuition as to how our approach is analogous to learning a prior.

Keywords: Inverse Reinforcement Learning, Meta-Learning, Deep Learning

TL;DR: The applicability of inverse reinforcement learning is often hampered by the expense of collecting expert demonstrations; this paper seeks to broaden its applicability by incorporating prior task information through meta-learning.