arXiv:2602.01460v3 Announce Type: replace-cross Abstract: Policy-gradient methods are widely used in reinforcement learning, yet training often becomes unstable or slows down as learning progresses. We study this phenomenon through the noise-to-signal ratio (NSR) of a policy-gradient estimator, defined as the estimator variance (noise) normalized by the squared norm of the true gradient (signal). Our main result is that, for (i) finite-horizon linear systems with Gaussian policies and linear state-feedback, and (ii) finite-horizon polynomial systems with Gaussian policies and polynomial feedba
This paper addresses critical issues in policy-gradient methods which are fundamental to ongoing advancements in reinforcement learning, a core component of AI development.
Improved understanding and mitigation of policy-gradient instability will accelerate the development of more reliable and scalable AI systems, directly impacting the viability of advanced AI applications.
Better methods for robust reinforcement learning could enable AI systems to tackle more complex, real-world problems with fewer failures and faster training times.
- · AI research labs
- · Reinforcement learning developers
- · Robotics companies
- · Autonomous systems developers
- · Companies with unstable reinforcement learning deployments
- · Researchers relying on inefficient policy-gradient methods
More stable and efficient reinforcement learning algorithms become available for AI practitioners.
This leads to faster progress and deployment of AI agents in various applications, from industrial automation to complex decision-making systems.
The enhanced capability of AI agents could significantly accelerate the development of advanced AI and potentially shift economic paradigms through widespread automation.
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Read at arXiv cs.LG