Reviews: Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning

Post rebuttal: I now understand the middle ground this paper is positioned, and the difference to propositional OO representations where you don't necessarily care which instance of an object type you're dealing with, which significantly reduces the dimensionality of learning transition dynamics. But this is still similar to other work on graph neural networks for model learning in fully relational representations, like Relation Networks by Santoro et al., and Interaction Networks by Battaglia et al. which in worst case learn T * n * (n-1) relations for n objects for T types of relations. However, this paper does do a nice job of formalizing from the OO-MDP and Propositional MDP setting as opposed to the two papers I mentioned which do not, and focus on the physical dynamics case. I am willing to increase my score based on this, but still do not think it is novel enough to be accepted. This is very similar to relational MDPs, but they learn transition dynamics in this relational attribute space rather than real state space.