we want to emphasize that the goal of S PECTRL

We thank the reviewers for their helpful suggestions, and will do our best to incorporate them into our paper. It is true that LSTMs can be used to solve RL problems with non-Markovian specifications. As we show in our experiments, reward shaping is crucial for learning complex tasks. For example, how does achieving a sub-goal count compared to violating a constraint? These challenges are exactly what our system is designed to solve.