  We present a structured neural network architecture that is inspired by
linear time-varying dynamical systems. The network is designed to mimic the
properties of linear dynamical systems which makes analysis and control simple.
The architecture facilitates the integration of learned system models with
gradient-based model predictive control algorithms, and removes the requirement
of computing potentially costly derivatives online. We demonstrate the efficacy
of this modeling technique in computing autonomous control policies through
evaluation in a variety of standard continuous control domains.
