text: The paper proposes a method  for learning object representations from pixels and then use such representations for doing reinforcement learning.   This method is based on convnets that map raw pixels to a mask and feature map.  The mask contains information about the presence/absence of objects in different pixel locations and the feature map contains information about object appearance.  I believe that the current method can only learn and track simple objects in a constant background, a problem which is  well-solved in computer vision.  Specifically, a simple method such as "background subtraction" can easily infer the mask (the outlying pixels which correspond to moving objects)  while simple tracking methods (see a huge literature over decades on computer vision) can allow to track these objects across frames.   The authors completely ignore all this previous work  and their "related work" section  starts citing papers from 2016 and onwards!   Is it any benefit of learning objects with the current (very expensive) method compared to simple methods such as  "background subtraction"?  Furthermore, the paper is very badly written  since it keeps postponing the actual explanations to later sections (while these  sections eventually refer to the appendices).   This makes reading the paper very hard.  For example, during the early sections you  keep referring to a loss function which will allow for learning the objects, but you never really give the form of this loss (which you should as soon as  you mentioning it)  and the reader needs to search into the appendices to find out what is happening.   Also, experimental results are very preliminary and not properly analyzed.   For example the results in Figure 3 are unclear and need to be discussed in detail in the main text.