Stability-certified reinforcement learning: A control-theoretic perspective

Ming Jin; Javad Lavaei

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arXiv · 1810.11505

Stability-certified reinforcement learning: A control-theoretic perspective

Ming Jin·Javad Lavaei

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Abstract

We investigate the important problem of certifying stability of reinforcement learning policies when interconnected with nonlinear dynamical systems. We show that by regulating the input-output gradients of policies, strong guarantees of robust stability can be obtained based on a proposed semidefinite programming feasibility problem. The method is able to certify a large set of stabilizing controllers by exploiting problem-specific structures; furthermore, we analyze and establish its (non)conservatism. Empirical evaluations on two decentralized control tasks, namely multi-flight formation and power system frequency regulation, demonstrate that the reinforcement learning agents can have high performance within the stability-certified parameter space, and also exhibit stable learning behaviors in the long run.

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Ming Jin, Javad Lavaei. 2018-10-26. Stability-certified reinforcement learning: A control-theoretic perspective. https://arxiv.org/abs/1810.11505

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