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 V-formation controller's convergence rate and time to convergence.
@article{arxiv.2006.00680,
title = {Learning Distributed Controllers for V-Formation},
author = {Shouvik Roy and Usama Mehmood and Radu Grosu and Scott A. Smolka and Scott D. Stoller and Ashish Tiwari},
journal= {arXiv preprint arXiv:2006.00680},
year = {2020}
}