  Recently, the Successor Features and Generalized Policy Improvement (SF&GPI)
framework has been proposed as a method for learning, composing, and
transferring predictive knowledge and behavior. SF&GPI works by having an agent
learn predictive representations (SFs) that can be combined for transfer to new
tasks with GPI. However, to be effective this approach requires state features
that are useful to predict, and these state-features are typically
hand-designed. In this work, we present a novel neural network architecture,
"Modular Successor Feature Approximators" (MSFA), where modules both discover
what is useful to predict, and learn their own predictive representations. We
show that MSFA is able to better generalize compared to baseline architectures
for learning SFs and modular architectures
