做机器人控制或自动驾驶的团队，DWS解决了强化学习控制信号抖动这个老大难问题，无需增加模型复杂度就能提升安全性和成功率，值得在你们的仿真或实机任务上试试。
强化学习常产生高频振荡控制信号，影响物理部署的安全与稳定。显式动作分块虽能预测固定轨迹，但会扩大策略输出维度，导致优化困难。本文提出双窗口平滑（DWS）框架，通过隐式动作分块实现平滑连续控制，无需扩展动作空间。DWS包含执行窗口（确保物理平滑）和价值窗口（修正评论家偏差），并引入轻量级时序正则化器。在DeepMind控制套件、工业能源管理及视觉自动驾驶任务中，DWS超越现有方法，实现100%成功率。
Implicit Action Chunking for Smooth Continuous Control
Reinforcement learning often produces high-frequency oscillatory control signals that undermine the safety and stability required for physical deployment. Explicit action chunking addresses this by predicting fixed-horizon trajectories but scales the policy output dimension proportionally with the horizon length, leading to optimization difficulties and incompatibility with standard step-wise interaction. To overcome these challenges, this paper proposes Dual-Window Smoothing (DWS), an implicit action chunking framework for smooth continuous control. Unlike explicit methods, DWS enforces temporal coherence without expanding the action space. It uses a dual-window design: an execution window that ensures physical smoothness through deterministic modulation, and a value window that aligns temporal-difference targets over the horizon to correct critic bias caused by open-loop execution. DWS also includes a lightweight actor-side temporal regularizer based on first-order action differences to promote global continuity. This design effectively bridges the gap between temporal abstraction and reactive step-wise control. Experiments on benchmarks including the DeepMind Control Suite and industrial energy management tasks show that DWS outperforms state-of-the-art (SOTA) baselines. In complex vision-based autonomous driving tasks, DWS achieves smoother control, safer behavior with reduced jitter, and attains a 100% success rate.