Introduces space-filling one-factor-at-a-time designs that improve space-filling properties over MOFAT designs while retaining screening capability for deterministic computer experiments.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Zero-shot sim-to-real transfer of independently trained RL policies for cart-pole swing-up and stabilization is achieved via sensitivity-guided domain randomization, linear curriculum learning, and first-order action smoothing with Simulink switching logic.
citing papers explorer
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Space-Filling One-Factor-At-A-Time Designs
Introduces space-filling one-factor-at-a-time designs that improve space-filling properties over MOFAT designs while retaining screening capability for deterministic computer experiments.
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Zero-shot Transfer of Reinforcement Learning Control Policies for the Swing-Up and Stabilization of a Cart-Pole System
Zero-shot sim-to-real transfer of independently trained RL policies for cart-pole swing-up and stabilization is achieved via sensitivity-guided domain randomization, linear curriculum learning, and first-order action smoothing with Simulink switching logic.