  Despite ample motivation from costly exploration and limited trajectory data,
rapidly adapting to new environments with few-shot reinforcement learning (RL)
can remain a challenging task, especially with respect to personalized
settings. Here, we consider the problem of recommending optimal policies to a
set of multiple entities each with potentially different characteristics, such
that individual entities may parameterize distinct environments with unique
transition dynamics. Inspired by existing literature in meta-learning, we
extend previous work by focusing on the notion that certain environments are
more similar to each other than others in personalized settings, and propose a
model-free meta-learning algorithm that prioritizes past experiences by
relevance during gradient-based adaptation. Our algorithm involves
characterizing past policy divergence through methods in inverse reinforcement
learning, and we illustrate how such metrics are able to effectively
distinguish past policy parameters by the environment they were deployed in,
leading to more effective fast adaptation during test time. To study
personalization more effectively we introduce a navigation testbed to
specifically incorporate environment diversity across training episodes, and
demonstrate that our approach outperforms meta-learning alternatives with
respect to few-shot reinforcement learning in personalized settings.
