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 Ranking Policy Gradient (RPG) is a sample-efficient off-policy policy gradient method
 that learns optimal ranking of actions to maximize the return.
 RPG has the following practical advantages:
-- It is currently the most sample-efficient model-free algorithm for learning deterministic policies.
+- It is a sample-efficient model-free algorithm for learning deterministic policies.
 - It is effortless to incorporate any exploration algorithm to improve the sample-efficiency of RPG further.
-- It is possible to learn a single RPG agent (parameterized by one neural network) that adapts to dynamic action space.  
 
 This codebase contains the implementation of RPG using the
 [dopamine](https://github.com/google/dopamine) framework.