On the one hand, for existing RL-based methods, their results on FB15K-237 are generally lower than those on NELL-995 since FB15K-237 is more complex and arguably more difficult to design proper reward functions manually.
our framework reliefs this problem to some extent by dynamically learning superior reward functions, thus we make greater improvements on challenging FB15K-237.