Reviews: Fast deep reinforcement learning using online adjustments from the past

Summary: This paper proposes a method that can help an RL agent to rapidly adapt to experience in the replay buffer. The method is the combination of slow and general component (i.e. An interesting part of this proposed approach is that it slightly changes the replay buffer by adding trajectory information but get a good boost in the perfoamnce. In addition, the extensive number of experiments have been conducted in order to verify the claim of this paper. Comments and Questions - This paper, in general, is well-written (especially the related works, they actually talk about the relation and difference with previous works) except for the followings: -- Section 3.1 and 3.2 do not have smooth flow.