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Jeonggyu Huh

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Preprint Sep 2026

Amortized Feedback Planning: Turning Model-Based Rollouts into Executable Policies

Closed-loop planning accounts for future observation-dependent actions but can be expensive to repeat at deployment. Bellman-gradient (BG) refinement differentiates conditional rollouts through an existing actor, corrects the current action, and stores the result in an executable policy. A backward sweep reuses deploye...

Jeonggyu Huh · 0 citations
Preprint Aug 2026

Scalable Pontryagin-Guided Adjoint-to-Control Recovery for Constrained Dynamic Portfolio Choice

We study continuous-time multi-asset portfolio choice and consumption under smooth pointwise constraints, including state-dependent feasible sets. The method separates dynamic information acquisition from local constrained recovery. A pointwise-feasible neural actor generates reference rollouts; after training, its rea...

Jaegi Jeon, Jeonggyu Huh, H. Koo et al. · 2 citations
Preprint Aug 2026

Self-Consistent Adjoint Policy Iteration for Constrained Dynamic Portfolio Choice

In the high-precision $50$--$50$ occupancy/broad-anchor design of a three-factor, fifty-asset benchmark, current-policy re-evaluation outperforms matched pooled refinement under the on-policy and broad evaluation laws.

Jeonggyu Huh, Yeoneung Kim, Seungwon Jeong · 0 citations

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