  This paper introduces Personalized Path Recourse, a novel method that
generates recourse paths for a reinforcement learning agent. The goal is to
edit a given path of actions to achieve desired goals (e.g., better outcomes
compared to the agent's original path) while ensuring a high similarity to the
agent's original paths and being personalized to the agent. Personalization
refers to the extent to which the new path is tailored to the agent's observed
behavior patterns from their policy function. We train a personalized recourse
agent to generate such personalized paths, which are obtained using reward
functions that consider the goal, similarity, and personalization. The proposed
method is applicable to both reinforcement learning and supervised learning
settings for correcting or improving sequences of actions or sequences of data
to achieve a pre-determined goal. The method is evaluated in various settings.
Experiments show that our model not only recourses for a better outcome but
also adapts to different agents' behavior.
