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arXiv 1306.6572cond-mat.stat-mechcs.SYeess.SYmath-phmath.MPmath.OC
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随机最优控制作为非平衡统计力学:密度与电流的变分法

Stochastic Optimal Control as Non-equilibrium Statistical Mechanics: Calculus of Variations over Density and Current

Wayne State University(韦恩州立大学);LANL(洛斯阿拉莫斯国家实验室);Radboud University(拉德堡德大学)

Vladimir Y. Chernyak, Michael Chertkov, Joris Bierkens, Hilbert J. Kappen

AI总结:

本文将随机最优控制扩展为包含动力学成本的非平衡统计力学框架,通过密度和电流的变分法推导广义规范不变的哈密顿-雅可比-贝尔曼方程,并讨论了非平衡时空复杂性的示例。

摘要:

在随机最优控制(SOC)中,最小化平均的期望成本,该成本包括控制成本(努力量)、空间成本(系统应处于何处)和目标成本(系统应到达何处),针对参与受控朗之万动力学的系统。我们通过引入由向量势表征的额外动力学成本扩展SOC问题,提出作为密度和电流变分的广义规范不变哈密顿-雅可比-贝尔曼方程的推导,建议流体动力学解释,并讨论了例如粒子在圆内 ergodic 控制的例子,阐明非平衡时空复杂性。

英文摘要:

In Stochastic Optimal Control (SOC) one minimizes the average cost-to-go, that consists of the cost-of-control (amount of efforts), cost-of-space (where one wants the system to be) and the target cost (where one wants the system to arrive), for a system participating in forced and controlled Langevin dynamics. We extend the SOC problem by introducing an additional cost-of-dynamics, characterized by a vector potential. We propose derivation of the generalized gauge-invariant Hamilton-Jacobi-Bellman equation as a variation over density and current, suggest hydrodynamic interpretation and discuss examples, e.g., ergodic control of a particle-within-a-circle, illustrating non-equilibrium space-time complexity.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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