An actor-critic framework built on a time-inhomogeneous little q-function and conditional normalizing flows serves as a mesh-free solver for entropy-regularized jump-diffusion control problems and stochastic games.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
A deep policy iteration method reformulates finite-horizon mean-field games as regenerative problems with deterministic cycles, using particle systems and one-step updates to handle dimensions up to 10,000 efficiently.
DBR reformulates backward losses via conditional expectations and Monte Carlo averaging to create smoother training targets for deep neural network solvers of high-dimensional nonlinear PDEs, yielding competitive benchmarks and half-order convergence under stated assumptions.
citing papers explorer
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An Actor-Critic Framework for Continuous-Time Jump-Diffusion Controls with Normalizing Flows
An actor-critic framework built on a time-inhomogeneous little q-function and conditional normalizing flows serves as a mesh-free solver for entropy-regularized jump-diffusion control problems and stochastic games.
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Deep Policy Iteration for High-Dimensional Mean-Field Games with Regenerative Reformulation
A deep policy iteration method reformulates finite-horizon mean-field games as regenerative problems with deterministic cycles, using particle systems and one-step updates to handle dimensions up to 10,000 efficiently.
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A deep backward regression-based scheme for high-dimensional nonlinear partial differential equations
DBR reformulates backward losses via conditional expectations and Monte Carlo averaging to create smoother training targets for deep neural network solvers of high-dimensional nonlinear PDEs, yielding competitive benchmarks and half-order convergence under stated assumptions.