Learning representations of sets of nodes in a graph is crucial for applications ranging from node-role discovery to link prediction and molecule classification.

Instead of using an explicit reward function, Inverse Reinforcement Learning (IRL; Ng et al., 2000) seeks to recover the reward by observing anexpert,e.g.,anhuman whoalready knowshowtoperform atask. However,most existing IRL algorithms assume that the transition model, and in some cases, the expert's policy, areknown.

Our solution to this task is simple: we split the latent codez into many independent blocks, and regularize the generator so that each block affects only a particular part of the image.