The article, authored by Jongmin Lee and Ernest K. Ryu from the Department of Mathematical Science at Seoul National University, focuses on advanced mathematical methods in artificial intelligence.

The study reveals that Anc-VI achieves a convergence rate of O(1/k) when the discount factor approaches 1, significantly improving upon the standard Value Iteration's rate.

The paper includes several supplementary materials, such as omitted proofs and discussions on broader impacts and limitations, reinforcing the rigor of the presented methodologies.

A complexity lower bound is established for Anc-VI, confirming its optimal acceleration rate and matching the upper bound.

It introduces Anchored Value Iteration, detailing its methodology and accelerated rates for Bellman consistency and optimality operators.

The conclusion summarizes the findings and emphasizes their significance, while also acknowledging contributions and disclosing funding sources.

The work is accessible on arXiv, published under the CC BY 4.0 DEED license, allowing for broad dissemination and collaboration in the field.

This accelerated iteration converges to a fixed point even when the discount factor equals 1, indicating a notable advancement over classical methods.

The authors address the broader impacts and limitations of their research, emphasizing the importance of ethical considerations in applying their methods.

Future research directions include examining the empirical effectiveness of Anc-VI and exploring its applications in model-free settings.

The research was supported by the Information & Communications Technology Planning & Evaluation (IITP) grant from the Korean government and the Samsung Science and Technology Foundation.

This research was presented at the 37th Conference on Neural Information Processing Systems (NeurIPS 2023), highlighting its relevance in the current academic discourse.