الباحثون

Songyuan Zhang

المنشورات 2

نسخة أولية وصول مفتوح

FAITH: Feasibility-Aware Safety-Filtered RL for High-Dimensional Systems

Safe reinforcement learning commonly places safety and task performance in the same policy objective, where they can introduce competing updates. Safety filters separate them at action execution, but classical designs require an analytic safety function and dynamics model, and standard minimal-intervention filters are …

نسخة أولية وصول مفتوح

LASER: Latent Space Adjoint Matching for Support-Constrained Entropy-Regularized Offline RL

Songyuan Zhang, Oswin So, Eric Yang Yu وآخرون · 2026

While offline reinforcement learning (RL) enables policy optimization from static datasets without costly online interaction, it remains bottlenecked by the risk of executing out-of-distribution (OOD) actions. Recent approaches mitigate this by learning a behavior-cloning policy through flow matching and then performin …

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