The paper proposes the ANJD flow and AVNSG operator to generate càdlàg trajectories via sequential MMD-gradient descent in Marcus-signature RKHS with generalisation bounds.
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ARL lifts states into signature-augmented manifolds and employs self-consistent proxies of future path-laws to enable deterministic expected-return evaluation while preserving contraction mappings in jump-diffusion environments.
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Generative Path-Law Jump-Diffusion: Sequential MMD-Gradient Flows and Generalisation Bounds in Marcus-Signature RKHS
The paper proposes the ANJD flow and AVNSG operator to generate càdlàg trajectories via sequential MMD-gradient descent in Marcus-signature RKHS with generalisation bounds.
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Anticipatory Reinforcement Learning: From Generative Path-Laws to Distributional Value Functions
ARL lifts states into signature-augmented manifolds and employs self-consistent proxies of future path-laws to enable deterministic expected-return evaluation while preserving contraction mappings in jump-diffusion environments.