  Soft actor-critic is a successful successor over soft Q-learning. While lived
under maximum entropy framework, their relationship is still unclear. In this
paper, we prove that in the limit they converge to the same solution. This is
appealing since it translates the optimization from an arduous to an easier
way. The same justification can also be applied to other regularizers such as
KL divergence.
