arXiv:2606.00431v1 Announce Type: new Abstract: We prove a variance-sensitive regret bound for Thompson sampling in stochastic generalised linear bandits. The argument assumes a warm-up, after which the regret is controlled through using the Gaussian Poincar\'e inequality. This bypasses the point at which previous optimism-based analyses break down. Removing the warm-up while retaining the same variance-sensitive scaling remains open, and appears nontrivial.
The paper demonstrates incremental progress in the theoretical foundations of reinforcement learning, specifically addressing limitations in Thompson sampling, a key algorithm in AI exploration-exploitation problems.
Improved theoretical understanding and robustness for algorithms like Thompson sampling can lead to more efficient and reliable AI systems, particularly in applications requiring adaptive decision-making under uncertainty.
This research refines the theoretical guarantees for a foundational AI algorithm, potentially enabling future advancements in its practical application by addressing previous analytical 'breakdown points'.
- · AI researchers
- · Machine learning developers
- · Sectors using reinforcement learning
The theoretical robustness of Thompson sampling in generalized linear bandits improves, facilitating more reliable algorithm design.
This improved theoretical foundation could lead to more robust and efficient AI agents and decision-making systems in various applications.
Enhanced algorithmic reliability might accelerate the deployment of autonomous systems with better performance in complex, uncertain environments.
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Read at arXiv cs.LG