Non-normal transient amplification is an important contributor to closed-loop variance in RL, and input-side suppression can reduce downstream covariance without altering peak gain.
arXiv preprint arXiv:2012.06644 , year =
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
Dual-Window Smoothing uses an execution window for deterministic smoothness and a value window to correct critic bias, plus a first-order temporal regularizer, to achieve smoother RL control than explicit chunking or standard baselines.
citing papers explorer
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Input-Side Variance Suppression under Non-Normal Transient Amplification in Continuous-Control Reinforcement Learning
Non-normal transient amplification is an important contributor to closed-loop variance in RL, and input-side suppression can reduce downstream covariance without altering peak gain.
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Implicit Action Chunking for Smooth Continuous Control
Dual-Window Smoothing uses an execution window for deterministic smoothness and a value window to correct critic bias, plus a first-order temporal regularizer, to achieve smoother RL control than explicit chunking or standard baselines.