text: The authors extend the approach proposed in the  "Reverse Curriculum Learning for Reinforcement Learning" paper by adding a discriminator that gives a bonus reward to a state based on how likely it thinks the current policy is to reach the goal from said state.  The discriminator is a potentially interesting mechanism to approximate multi-step backups in sparse-reward environments.  The approach of this paper seems severely severely limited by the assumptions made by the authors, mainly assuming a deterministic environment, known goal states and the ability to sample anywhere in the state space.  Some of these assumptions may be reasonable in domains such as robotics,  but they seem very restrictive in the domains like the games considered in the paper. Additional Comments: -The authors demonstrate some benefits of using Tendency rewards,  but made little attempt to explain why it leads to accelerated learning.  Results are pure performance results. -The authors should probably structure the tendency reward as potential based instead of using the Gaussian kernel hack they introduce in section 4.2 - Presentation: There are several mistakes and formatting issues in References - Assumption 2 transformations -> transitions? -Need to add assumption 3: advance knowledge of goal state - the use of gamma as  a scale factor in equation 2 is confusion,  it was already introduced as the discount factor ( which is default notation in RL).  It also isn't clear what the notation r_f denotes (is it the same as r^f in appendix?). -It is nice to see that the authors compare their method with alternative approaches.  Unfortunately, the proposed method does not seem to offer many benefits.