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Parametric Resonance in Primal-Dual Learning for Slowly Non-Stationary Constrained MDPs

Paper recorded by Signals 4 on 2026-10-16 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-16 on arXiv · recorded by Signals 4 on 2026-10-19

Category: cs.LG · 机器学习 · first seen 2026-10-19

Abstract

Primal-dual methods for constrained Markov decision processes are analyzed under a two-timescale assumption, with non-stationarity entering through two channels. In the additive channel, where the constraint level oscillates, the dual oscillation saturates at a floor set by the moving optimum and is amplified linearly by the timescale ratio. In the parametric channel, where the constraint sensitiv

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#257 most recent of 749 cs.LG papers we have recorded · ↑ newer: Task-aware Kernel Stein certificates for Rényi differential privacy · ↓ older: Sampling the XY model with an attention-based neural network
Cite this page: Parametric Resonance in Primal-Dual Learning for Slowly Non-Stationary Constrained MDPs: the #257 most recent of 749 cs.LG papers we have recorded (as of 2026-10-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/parametric-resonance-in-primal-dual-learning-for-slowly-non-stationary-constrain.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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