Author

Mintae Kim

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#machine learning Preprint Sep 2026

In-Distribution Imagination for Model-Based Offline Reinforcement Learning

Model-based offline reinforcement learning (MBORL) improves sample efficiency through model-generated trajectories. However, accumulative model error can drive imagined trajectories outside the offline data distribution, leading to unrealistic synthetic data and unstable policy optimization. Many existing methods prima...

Mintae Kim, K. Sreenath · 0 citations

#machine learning Jul 2026

Trajectory-Regularized Stochastic Optimal Control via KL Divergence

TRSOC is introduced, which augments standard stochastic optimal control with a Kullback--Leibler (KL) divergence between controlled and reference trajectory distributions, and the regularization parameter induces a trade-off between performance-driven and reference-preserving behavior.

Mintae Kim, K. Sreenath · arXiv.org · 0 citations