Cocktail: Learn a Better Neural Network Controller from Multiple Experts via Adaptive Mixing and Robust Distillation

Neural networks are being increasingly applied to control and decision making for learning-enabled cyber-physical systems (LE-CPSs). They have shown promising performance without requiring the development of complex physical models; however, their adoption is significantly hindered by the concerns o...

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Bibliographic Details
Published in:2021 58th ACM/IEEE Design Automation Conference (DAC) pp. 397 - 402
Main Authors: Wang, Yixuan, Huang, Chao, Wang, Zhilu, Xu, Shichao, Wang, Zhaoran, Zhu, Qi
Format: Conference Proceeding
Language:English
Published: IEEE 05.12.2021
Subjects:
Adaptive systems
Energy measurement
Neural networks
Reinforcement learning
Robustness
Time measurement
Weight measurement
Online Access:Get full text
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Summary:Neural networks are being increasingly applied to control and decision making for learning-enabled cyber-physical systems (LE-CPSs). They have shown promising performance without requiring the development of complex physical models; however, their adoption is significantly hindered by the concerns on their safety, robustness, and efficiency. In this work, we propose COCKTAIL, a novel design framework that automatically learns a neural network based controller from multiple existing control methods (experts) that could be either model-based or neural network based. In particular, COCKTAIL first performs reinforcement learning to learn an optimal system-level adaptive mixing strategy that incorporates the underlying experts with dynamically-assigned weights, and then conducts a teacher-student distillation with probabilistic adversarial training and regularization to synthesize a student neural network controller with improved control robustness (measured by a safe control rate metric with respect to adversarial attacks or measurement noises), control energy efficiency, and verifiability (measured by the computation time for verification). Experiments on three non-linear systems demonstrate significant advantages of our approach on these properties over various baseline methods.
DOI:10.1109/DAC18074.2021.9586148