CARLOS employs an aggregate deep neural network trained on progressively finer time grids with adaptive sampling to learn continuous-time exercise boundaries for optimal stopping, delivering higher values than discrete Bermudan methods.
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2026 2verdicts
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The work rewrites the controlled Fokker-Planck equation as deterministic characteristic dynamics for state-independent diffusion and derives first-order necessary conditions for optimal control with state-dependent terminal times and distributional constraints.
citing papers explorer
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Continuous-time Optimal Stopping through Deep Reinforcement Learning
CARLOS employs an aggregate deep neural network trained on progressively finer time grids with adaptive sampling to learn continuous-time exercise boundaries for optimal stopping, delivering higher values than discrete Bermudan methods.
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Nonlinear Stochastic Optimal Control and Optimal Stopping using the Fokker-Planck Transformation
The work rewrites the controlled Fokker-Planck equation as deterministic characteristic dynamics for state-independent diffusion and derives first-order necessary conditions for optimal control with state-dependent terminal times and distributional constraints.