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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Discrete decentralized learning dynamics on manifolds converge uniformly to an overdamped Langevin SDE whose stationary states produce orthogonally disentangled, linearly separable features.
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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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Continuous Limits of Coupled Flows in Representation Learning
Discrete decentralized learning dynamics on manifolds converge uniformly to an overdamped Langevin SDE whose stationary states produce orthogonally disentangled, linearly separable features.