Before revision:  TASAC adds a second-stage binary policy to choose between the previous action and the action output by an SAC actor. It has two benefits compared to traditional off-policy RL algorithms : persistent exploration and an unbiased multi-step Q operator for TD learning .
After revision:  TASAC adds a second-stage binary policy to choose between the previous action and the action output by an actor. Crucially, its act-or-repeat decision hinges on the actually sampled action instead of the expected behavior of the actor. This post-acting switching scheme let the overall policy make more informed decisions. TAAC has two important features : persistent exploration and an unbiased multi-step Q operator for TD learning .