AI RESEARCH
A Schr\"odinger Eigenfunction Method for Long-Horizon Stochastic Optimal Control
arXiv CS.LG
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ArXi:2603.23173v1 Announce Type: new High-dimensional stochastic optimal control (SOC) becomes harder with longer planning horizons: existing methods scale linearly in the horizon $T$, with performance often deteriorating exponentially. We overcome these limitations for a subclass of linearly-solvable SOC problems-those whose uncontrolled drift is the gradient of a potential. In this setting, the Hamilton-Jacobi-Bellman equation reduces to a linear PDE governed by an operator $\mathcal{L