In the natural sciences, physics has found great success by describing the universe in terms of symmetry preserving transformations. Inspired by this formalism, we propose a framework, built upon the theory of group representation, for learning representations of a dynamical environment structured around the transformations that generate its evolution. Experimentally, we learn the structure of explicitly symmetric environments without supervision from observational data generated by sequential interactions.

Human explanation (e.g.,interms offeature importance) hasbeenrecently used to extend the communication channel between human and agent in interactive machinelearning.

We adopt four bioinformatics datasets in the experiment. Given the input graph, it will randomly add or cut a certain portion ofconnections between nodes withtheprobability of0.2. It will set the feature of 20% nodes in the graph to Gaussian noises with mean and standard deviation is 0.5. We adopt the Adam [5] optimizer, which is a variant of Stochastic Gradient Descent (SGD) with adaptivemoment estimation.