arXiv:2601.22970v2 Announce Type: replace Abstract: Policies learned via continuous actor-critic methods often exhibit erratic, high-frequency oscillations, making them unsuitable for physical deployment. Current approaches attempt to enforce smoothness by directly regularizing the policy's output. We argue that this approach treats the symptom rather than the cause. In this work, we theoretically establish that policy non-smoothness is fundamentally governed by the differential geometry of the critic. By applying implicit differentiation to the actor-critic objective, we prove that the sensit
This research addresses a long-standing challenge in actor-critic methods, indicating a maturing field focused on practical deployment of AI systems.
Improved stability and smoothness in AI policies are critical for reliable applications in physical systems, which could accelerate the adoption of advanced robotics and autonomous agents.
The understanding of policy non-smoothness shifts from output regularization to the critic's differential geometry, offering a more fundamental approach to stability.
- · AI researchers
- · Robotics developers
- · Companies deploying autonomous systems
- · Developers reliant on ad-hoc policy smoothing techniques
More robust and predictable AI-driven control systems emerge, particularly in areas requiring physical interaction.
Increased trust and adoption of AI in safety-critical applications due to enhanced reliability and reduced erratic behavior.
Accelerated development and commercialization of complex autonomous robots and agents, potentially leading to new economic efficiencies and capabilities.
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Read at arXiv cs.LG