Learning stabilizing policies in stochastic control systems
Zikelic D, Lechner M, Chatterjee K, Henzinger TA. Learning stabilizing policies in stochastic control systems. arXiv, 10.48550/arXiv.2205.11991.
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Abstract
In this work, we address the problem of learning provably stable neural
network policies for stochastic control systems. While recent work has
demonstrated the feasibility of certifying given policies using martingale
theory, the problem of how to learn such policies is little explored. Here, we
study the effectiveness of jointly learning a policy together with a martingale
certificate that proves its stability using a single learning algorithm. We
observe that the joint optimization problem becomes easily stuck in local
minima when starting from a randomly initialized policy. Our results suggest
that some form of pre-training of the policy is required for the joint
optimization to repair and verify the policy successfully.
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2022-05-24
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arXiv
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Cite this
Zikelic D, Lechner M, Chatterjee K, Henzinger TA. Learning stabilizing policies in stochastic control systems. arXiv. doi:10.48550/arXiv.2205.11991
Zikelic, D., Lechner, M., Chatterjee, K., & Henzinger, T. A. (n.d.). Learning stabilizing policies in stochastic control systems. arXiv. https://doi.org/10.48550/arXiv.2205.11991
Zikelic, Dorde, Mathias Lechner, Krishnendu Chatterjee, and Thomas A Henzinger. “Learning Stabilizing Policies in Stochastic Control Systems.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2205.11991.
D. Zikelic, M. Lechner, K. Chatterjee, and T. A. Henzinger, “Learning stabilizing policies in stochastic control systems,” arXiv. .
Zikelic D, Lechner M, Chatterjee K, Henzinger TA. Learning stabilizing policies in stochastic control systems. arXiv, 10.48550/arXiv.2205.11991.
Zikelic, Dorde, et al. “Learning Stabilizing Policies in Stochastic Control Systems.” ArXiv, doi:10.48550/arXiv.2205.11991.
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arXiv 2205.11991