Author

Minghao Yang

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Perturbation-Resilient Autonomous Navigation with Distributionally Robust Reinforcement Learning

DRIQN is proposed to integrate Distributionally Robust Optimization (DRO) with implicit quantile networks to optimize worst-case performance under natural environmental conditions and incorporates heterogeneous noise sources and target robustness-critical scenarios.

Zhaofan Zhang, Minghao Yang, Sihong Xie et al. · 0 citations

Preprint Jul 2026

Branch-JEPA: Finite-Support Predictive Distributions for JEPA World Models

Branch-JEPA is introduced, which replaces this point-valued transition with a context-weighted finite set of latent successors, and preserves more distinct futures, while full-set scoring improves the quality of the resulting predictive distribution.

Zhi Song, Ximing Xing, Zhenchao Tang et al. · 0 citations