Uncertainty quantification for Markov chain induced martingales with application to temporal difference learning
arXiv:2502.13822v3 Announce Type: replace-cross Abstract: We establish novel and general high-dimensional concentration inequalities and Berry-Esseen bounds for vector-valued martingales induced by Markov chains. We apply these results to analyze the performance of the Temporal Difference (TD) learning algorithm with linear function approximations, a widely used method for policy evaluation in Reinforcement Learning (RL), obtaining a sharp high-probability consistency guarantee that matches the asymptotic variance up to logarithmic factors. Furthermore, we establish an $O(T^{-\frac{1}{4}}\log
The continuous academic advancements in AI, particularly in Reinforcement Learning theory, are driving improvements in algorithm robustness and reliability.
Improved uncertainty quantification for RL algorithms is critical for their deployment in high-stakes environments, increasing trust and accelerating adoption in real-world applications.
The theoretical underpinnings of Temporal Difference learning are becoming more robust, allowing for more predictable and reliable performance guarantees in complex systems.
- · AI/ML researchers
- · Reinforcement Learning applications
- · Autonomous systems developers
- · Traditional control systems
- · Trial-and-error RL deployments
Increased reliability of AI agents in dynamic environments.
Faster and safer deployment of AI agents across various industries, from logistics to robotics.
Enhanced competition in applied AI, shifting focus from raw performance to provable safety and robustness.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG