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Risk Sensitive Path Integral Control

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arxiv 1203.3523 v1 pith:BO7SMU25 submitted 2012-03-15 cs.SY cs.SYmath.OC

classification cs.SYmath.OC
keywords controlriskintegralpathmethodsoptimalsensitivestochastic
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Recently path integral methods have been developed for stochastic optimal control for a wide class of models with non-linear dynamics in continuous space-time. Path integral methods find the control that minimizes the expected cost-to-go. In this paper we show that under the same assumptions, path integral methods generalize directly to risk sensitive stochastic optimal control. Here the method minimizes in expectation an exponentially weighted cost-to-go. Depending on the exponential weight, risk seeking or risk averse behaviour is obtained. We demonstrate the approach on risk sensitive stochastic optimal control problems beyond the linear-quadratic case, showing the intricate interaction of multi-modal control with risk sensitivity.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Factor Graph Approach to Scalable Multi-Output Gaussian Process Regression

    cs.LG 2026-08 conditional novelty 6.0 of 10

    An LMC multi-output GP is approximated by a one-dimensional chain model on which exact Gaussian message passing delivers linear-time inference and native partial-observation handling.

  2. Path Integral Methods for Synthesizing and Preventing Stealthy Attacks in Nonlinear Cyber-Physical Systems

    eess.SY 2025-04 conditional novelty 5.0 of 10

    A path integral Monte Carlo framework is developed for synthesizing worst-case stealthy attacks on nonlinear continuous-time control systems and for computing saddle-point defensive policies.

  3. Advancing Frontiers of Path Integral Theory for Stochastic Optimal Control

    math.OC 2025-04 conditional novelty 4.0 of 10

    This dissertation derives path integral controllers for chance-constrained control, zero-sum games, hierarchical tasks, deception, and stealthy attacks, and gives a sample complexity bound for discrete-time LQR.

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