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This paper provides rigorous mathematical analysis of sensitivity decay in discrete-time Pontryagin systems, establishing uniform bounds for how terminal reward perturbations affect optimal control trajectories. By proving that these effects decay exponentially with the horizon length, the work offers theoretical guarantees relevant to long-horizon reinforcement learning and optimal control algorithms used in AI agents.
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Horizon-Uniform Sensitivity and Decay of Terminal Reward Perturbations in Discrete-Time Pontryagin Systems

arXiv3 min read

arXiv:2606.17762v3 Announce Type: replace-cross Abstract: We study local stationary solutions of finite-horizon discrete-time Pontryagin systems near a steady extremal. Suppose that the stationarity equation for the control is regular, the reduced state--costate map is hyperbolic, and the endpoint conditions satisfy a scaled transversality condition with respect to the stable and unstable subspaces. Then the linearized boundary-value problem admits an inverse whose Green estimate is uniform in the horizon. The Green kernel separates interior decay from the two reflections induced by the endpoint conditions. For $x_0=x_{\rm in}$ and $p_T=r_x(x_T,y)$, a contraction argument in a weighted norm proves existence and uniqueness in a neighborhood independent of $T$, together with uniform Lipschitz estimates and a pointwise quadratic remainder. We also derive an explicit admissible data radius and an a posteriori criterion for existence and local uniqueness near an approximate trajectory. For these graph boundary conditions, a one-sided Green estimate shows that a perturbation of the terminal reward changes the initial control and the gradient with respect to the initial state of the stationary objective by $O(e^{-\alpha_{\rm ter} T})$ for every $\alpha_{\rm ter}$ below the dichotomy rate. For linear-quadratic systems with invertible $A$, stabilizable $(A,B)$, $Q\succ0$, $R\succ0$, and a nonpositive terminal Hessian, a symplectic graph condition verifies the assumptions, and the finite-horizon Riccati matrix and initial feedback gain converge at rate $O(e^{-2\gamma T})$. Numerical experiments verify the certificates and the predicted decay rates.

Abstract:We study local stationary solutions of finite-horizon discrete-time Pontryagin systems near a steady extremal. Suppose that the stationarity equation for the control is regular, the reduced state--costate map is hyperbolic, and the endpoint conditions satisfy a scaled transversality condition with respect to the stable and unstable subspaces. Then the linearized boundary-value problem admits an inverse whose Green estimate is uniform in the horizon. The Green kernel separates interior decay from the two reflections induced by the endpoint conditions. For $x_0=x_{\rm in}$ and $p_T=r_x(x_T,y)$, a contraction argument in a weighted norm proves existence and uniqueness in a neighborhood independent of $T$, together with uniform Lipschitz estimates and a pointwise quadratic remainder. We also derive an explicit admissible data radius and an a posteriori criterion for existence and local uniqueness near an approximate trajectory. For these graph boundary conditions, a one-sided Green estimate shows that a perturbation of the terminal reward changes the initial control and the gradient with respect to the initial state of the stationary objective by $O(e^{-\alpha_{\rm ter} T})$ for every $\alpha_{\rm ter}$ below the dichotomy rate. For linear-quadratic systems with invertible $A$, stabilizable $(A,B)$, $Q\succ0$, $R\succ0$, and a nonpositive terminal Hessian, a symplectic graph condition verifies the assumptions, and the finite-horizon Riccati matrix and initial feedback gain converge at rate $O(e^{-2\gamma T})$. Numerical experiments verify the certificates and the predicted decay rates.

Optimization and Control (math.OC); Artificial Intelligence (cs.AI)

arXiv:2606.17762 [math.OC]

(or arXiv:2606.17762v3 [math.OC] for this version)

https://doi.org/10.48550/arXiv.2606.17762 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 320 words

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