  Recent years have seen an emerging interest in the trustworthiness of machine
learning-based agents in the wild, especially in robotics, to provide safety
assurance for the industry. Obtaining behavioral guarantees for these agents
remains an important problem. In this work, we focus on guaranteeing a
model-based planning agent reaches a goal state within a specific future time
step. We show that there exists a lower bound for the reward at the goal state,
such that if the said reward is below that bound, it is impossible to obtain
such a guarantee. By extension, we show how to enforce preferences over
multiple goals.
