Learning Distributed Controllers for V-Formation

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...

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Bibliographic Details
Published in:arXiv.org 2020-06
Main Authors: Roy, Shouvik, Mehmood, Usama, Grosu, Radu, Smolka, Scott A, Stoller, Scott D, Tiwari, Ashish
Format: Article
Language:English
Subjects:
Confidence intervals
Controllers
Convergence
Machine learning
Predictive control
Retraining
Statistical analysis
Statistical models
Online Access:Get full text
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Summary: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.
ISSN:2331-8422