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Robust Reinforcement Learning Control Using Integral Quadratic Constraints for Recurrent Neural Networks

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6 Author(s)
Anderson, C.W. ; Colorado State Univ., Fort Collins ; Young, P.M. ; Buehner, M.R. ; Knight, J.N.
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The applicability of machine learning techniques for feedback control systems is limited by a lack of stability guarantees. Robust control theory offers a framework for analyzing the stability of feedback control loops, but for the integral quadratic constraint (IQC) framework used here, all components are required to be represented as linear, time-invariant systems plus uncertainties with, for IQCs used here, bounded gain. In this paper, the stability of a control loop including a recurrent neural network (NN) is analyzed by replacing the nonlinear and time-varying components of the NN with IQCs on their gain. As a result, a range of the NN's weights is found within which stability is guaranteed. An algorithm is demonstrated for training the recurrent NN using reinforcement learning and guaranteeing stability while learning.

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Neural Networks, IEEE Transactions on  (Volume:18 ,  Issue: 4 )