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Risk Sensitive Path Integral Control
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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.
Forward citations
Cited by 3 Pith papers
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A Factor Graph Approach to Scalable Multi-Output Gaussian Process Regression
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Path Integral Methods for Synthesizing and Preventing Stealthy Attacks in Nonlinear Cyber-Physical Systems
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.
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Advancing Frontiers of Path Integral Theory for Stochastic Optimal Control
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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