arXiv:2606.02645v1 Announce Type: cross Abstract: Periodic target updates in Q-learning and soft target updates in actor-critic methods are empirically well established stabilization mechanisms, but their precise theoretical explanation is still incomplete. This paper gives a rigorous and exact analysis of these mechanisms for Q-learning with linear function approximation (linear Q-learning) using the exact switched linear system (SLS) dynamics induced by the Bellman maximum and the joint spectral radius (JSR) of the resulting switching matrix families. Although linear Q-learning can fail to c
This research provides a theoretical understanding for empirically established stabilization mechanisms in Q-learning, emerging as AI models scale and stability in reinforcement learning becomes more critical.
Improved theoretical understanding of Q-learning stabilization contributes to more reliable and predictable AI development, potentially accelerating the deployment of advanced autonomous systems.
The theoretical foundation for Q-learning stability is strengthened, which could lead to more robust and generalized reinforcement learning algorithms in practical applications.
- · AI researchers and developers
- · Robotics companies
- · Autonomous systems developers
- · Companies with unstable Q-learning implementations
- · Theoretical models lacking rigor
Refined Q-learning algorithms will emerge with better performance guarantees.
This stability will enable more complex, real-world applications of reinforcement learning, such as advanced AI agents or robotics.
Increased reliability and predictability of AI could reduce deployment risks and accelerate adoption across critical industries.
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