Dissipative Imitation Learning for Discrete Dynamic Output Feedback
  Control with Sparse Data Sets
Amy K. Strong Ethan J. LoCicero Leila J. Bridgeman
Imitation learning enables the synthesis of controllers for complex
objectives and highly uncertain plant models. However, methods to provide
stability guarantees to imitation learned controllers often rely on large
amounts of data and/or known plant models. In this paper, we explore an
input-output (IO) stability approach to dissipative imitation learning, which
achieves stability with sparse data sets and with little known about the plant
model. A closed-loop stable dynamic output feedback controller is learned using
expert data, a coarse IO plant model, and a new constraint to enforce
dissipativity on the learned controller. While the learning objective is
nonconvex, iterative convex overbounding (ICO) and projected gradient descent
(PGD) are explored as methods to successfully learn the controller. This new
imitation learning method is applied to two unknown plants and compared to
traditionally learned dynamic output feedback controller and neural network
controller. With little knowledge of the plant model and a small data set, the
dissipativity constrained learned controller achieves closed loop stability and
successfully mimics the behavior of the expert controller, while other methods
often fail to maintain stability and achieve good performance.