This paper introduces Personalized Path Recourse, a novel method that
generates recourse paths for an agent. The objective is to achieve desired
goals (e.g., better outcomes compared to the agent's original paths of action),
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 and demonstrates promising
results.

论文介绍了个性化路径补救（Personalized Path Recourse）这一新方法，旨在为代理生成补救路径，以实现期望的目标并保持与代理原始路径的高相似性，并根据代理的观察行为模式个性化定制新路径。该方法适用于纠正或改进行动或数据序列以达到预定目标的强化学习和监督学习设置，并在不同场景中进行了评估，显示出良好的结果。