arXiv:2605.16103v2 Announce Type: replace Abstract: Q-learning is known to suffer from overestimation bias: because the Bellman update maximizes noisy or imperfect action-value estimates, positive errors can be selected and propagated, causing learned values to exceed the true optimal values. This bias can slow learning, degrade policy quality, and make value estimates unreliable. Although the convergence of Q-learning has been studied extensively, convergence theory that explicitly reflects this overestimation mechanism remains limited. This paper studies the asymmetric convergence behavior o
The paper refines foundational AI algorithms, addressing known limitations that become more critical as AI systems are deployed in complex, real-world applications.
Improved understanding and mitigation of Q-learning's overestimation bias can lead to more robust, efficient, and reliable AI systems, particularly in reinforcement learning applications.
The explicit analysis of asymmetric convergence behavior provides a theoretical basis for developing more stable and faster-learning Q-learning variants, potentially accelerating AI development.
- · AI researchers
- · Reinforcement learning developers
- · Autonomous system manufacturers
Refined Q-learning algorithms are developed and implemented, leading to improved agent performance.
AI agents in various fields, from robotics to finance, exhibit more reliable decision-making and faster adaptation.
Increased trust and broader adoption of AI systems due to enhanced stability and predictability, impacting commercial applications.
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Read at arXiv cs.AI