On the other hand, Go-Explore Seq2Seq shows promising results by solving almost half of the unseen games. Figure FIGREF62 (in Appendix SECREF60) shows that most of the lost games are in the hardest set, where a very long sequence of actions is required for winning the game. These results demonstrate both the relative effectiveness of training a Seq2Seq model on Go-Explore trajectories, but they also indicate that additional effort needed for designing reinforcement learning algorithms that effectively generalize to unseen games. 
 Question: How do the authors show that their learned policy generalize better than existing solutions to unseen games?
promising results by solving almost half of the unseen games most of the lost games are in the hardest set, where a very long sequence of actions is required for winning the game