arXiv AI October 1, 2026 By Seokmin Ko, Taewon Goo, Kihyuk Hong

DAMPER: Return-Prioritized Gradient Control for Smooth Policies

DAMPER is a new technique for actor‑critic methods that reduces action oscillation in continuous control tasks. It combines the native actor gradient with a temporal‑consistency gradient using conflict‑conditioned projection and adaptive magnitude control, ensuring the auxiliary component aligns positively with the actor gradient. Experiments on TD3 and SAC across six tasks show that DAMPER consistently lowers oscillation compared to baseline agents and outperforms other methods in most task‑backbone pairs.

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