ZAPS-DA: Zero-Phase Action Policy Smoothing with Decoupled Actor for Continuous Control in Reinforcement Learning
arXiv:2605.30612v1 Announce Type: cross Abstract: Continuous control policies trained with off-policy reinforcement learning frequently exhibit high-frequency action jitter, rendering direct deployment on physical actuators impractical. Post-hoc filtering attenuates jitter but introduces phase lag; embedding smoothness penalties in the actor's loss couples them with the RL gradient and conflates reward regression with over-aggressive smoothing. We present ZAPS-DA, a framework that reduces action jitter at deployment with negligible phase lag and no post-processing. ZAPS-DA pairs an unmodified
The paper addresses a critical, long-standing hurdle in deploying continuous control policies from reinforcement learning onto physical systems, which is becoming increasingly relevant as AI moves from simulation to real-world robotics.
This development improves the practicality and safety of AI-driven continuous control, making advanced robotics and automation more viable for industrial and potentially domestic applications.
The ability to deploy reinforcement learning control policies with high fidelity and reduced jitter, without post-processing or significant phase lag, changes the barrier to entry for robust physical AI deployments.
- · Robotics manufacturers
- · Automation industries
- · AI hardware developers
- · Logistics and manufacturing sectors
- · Companies reliant on imprecise robotic control
- · Developers focused on purely simulation-based RL solutions
Robotics systems become more reliable and precise in real-world environments due to enhanced continuous control policies.
Accelerated adoption of AI in complex physical tasks across industries, leading to increased automation and efficiency gains.
The development of highly agile and precise humanoid robots with sophisticated continuous motor control, impacting labor markets and societal interactions.
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Read at arXiv cs.LG