Publications in Scientific Journals:

S. Roy, U. Mehmood, R. Grosu, S. Smolka, S. Stoller, A. Tiwari:
"Learning Distributed Controllers for V-Formation";
ArXiv, . (2020), 10 pages.

English abstract:
We show how a high-performing, fully distributed
and symmetric neural V-formation controller can be synthesized
from a Centralized MPC (Model Predictive Control) controller
using Deep Learning. This result is significant as we also establish
that under very reasonable conditions, it is impossible to achieve
V-formation using a deterministic, distributed, and symmetric
controller. The learning process we use for the neural V-formation
controller is significantly enhanced by CEGkR, a Counterexample-
Guided k-fold Retraining technique we introduce, which extends
prior work in this direction in important ways. Our experimental
results show that our neural V-formation controller generalizes
to a significantly larger number of agents than for which it
was trained (from 7 to 15), and exhibits substantial speedup
over the MPC-based controller. We use a form of statistical
model checking to compute confidence intervals for our neural Vformation
controllerīs convergence rate and time to convergence.

V-Formation, Model Predictive Control, Distributed Neural Controller, Deep Neural Network, Supervised Learning. I

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.