You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Table 1 summarizes handicaps leveraged in each algorithm and the performance of MC-LAVE-RL and baseline algorithms across 9 IF games included in the Jericho environment. The results show that MC-LAVE-RL outperforms or matches the state-of-the-art results on 8 out of 9 games. Although MC-LAVE-RL requires more handicap or assumption, it performs the same or better than strong baseline MC!Q*BERT which requires similar assumptions and more requirements. In addition, MC-LAVE-RL achieves higher game scores on overall games compared to PUCT-RL, which is a baseline algorithm that only excludes language-driven exploration strategy from MC-LAVE-RL. Furthermore, MC-LAVE-RL performs signiﬁcantly better than other methods on difﬁcult games such as Z ORK 1, D EEPHOME , and L UDICORP , which are categorized by Hausknecht et al. (2020) as a relatively challenging game due to the large action space and sparse rewards.

Question: Which of the baseline algorithms require the resettable simulator?
MC!Q*BERT and PUCT-RL require the resettable simulator assumption.