arXiv:2606.10835v1 Announce Type: new Abstract: Periodic hard target updates are among the most common stabilization devices in modern deep Q-learning. Recent studies suggest that target updates can improve stability in Q-learning with function approximation, including linear function approximation. We introduce and analyze the so-called $\lambda$-target update, obtained by averaging the $m$-periodic target update maps with $\lambda$-geometric weights $(1-\lambda)\lambda^{m-1}$, $\lambda \in [0,1]$. The endpoint $\lambda=0$ recovers the one-period target update, while the continuous endpoint $
This paper represents continued academic research into optimizing crucial underlying mechanisms for AI learning algorithms, indicating an ongoing push for more stable and efficient AI development.
Improved Q-learning stability can lead to more robust and reliable autonomous systems, benefiting fields reliant on AI agents or complex decision-making processes.
The proposed 'geometrically averaged hard target updates' offer a new method for stabilizing Q-learning, potentially influencing the design of future reinforcement learning algorithms.
- · AI researchers
- · Reinforcement learning developers
- · Autonomous system developers
This research provides a theoretical enhancement for the stability of Q-learning algorithms.
More stable reinforcement learning could accelerate the development and deployment of sophisticated AI agents.
Improved fundamental AI algorithms contribute to the broader advancement of AI capabilities across various applications, from robotics to complex decision systems.
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