Stochastic Optimal Control (SOC) problems arise in systems influenced by
uncertainty, such as autonomous robots or financial models. Traditional methods
like dynamic programming are often intractable for high-dimensional, nonlinear
systems due to the curse of dimensionality. This dissertation explores the path
integral control framework as a scalable, sampling-based alternative. By
reformulating SOC problems as expectations over stochastic trajectories, it
enables efficient policy synthesis via Monte Carlo sampling and supports
real-time implementation through GPU parallelization.
We apply this framework to six classes of SOC problems: Chance-Constrained
SOC, Stochastic Differential Games, Deceptive Control, Task Hierarchical
Control, Risk Mitigation of Stealthy Attacks, and Discrete-Time LQR. A sample
complexity analysis for the discrete-time case is also provided. These
contributions establish a foundation for simulator-driven autonomy in complex,
uncertain environments.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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