This content originally appeared on HackerNoon and was authored by Anchoring
:::info Authors:
(1) Jongmin Lee, Department of Mathematical Science, Seoul National University;
(2) Ernest K. Ryu, Department of Mathematical Science, Seoul National University and Interdisciplinary Program in Artificial Intelligence, Seoul National University.
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1.1 Notations and preliminaries
2.1 Accelerated rate for Bellman consistency operator
2.2 Accelerated rate for Bellman optimality opera
5 Approximate Anchored Value Iteration
6 Gauss–Seidel Anchored Value Iteration
7 Conclusion, Acknowledgments and Disclosure of Funding and References
4 Complexity lower bound
We now present a complexity lower bound establishing optimality of Anc-VI.
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\ The so-called “span condition” of Theorem 5 is arguably very natural and is satisfied by standard VI and Anc-VI. The span condition is commonly used in the construction of complexity lower bounds on first-order optimization methods [13, 14, 23, 25, 59, 65] and has been used in the prior state-ofthe-art lower bound for standard VI [37, Theorem 3]. However, designing an algorithm that breaks the lower bound of Theorem 5 by violating the span condition remains a possibility. In optimization theory, there is precedence of lower bounds being broken by violating seemingly natural and minute conditions [35, 40, 98].
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:::info This paper is available on arxiv under CC BY 4.0 DEED license.
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This content originally appeared on HackerNoon and was authored by Anchoring

Anchoring | Sciencx (2025-01-14T22:56:39+00:00) Anc-VI Sets a New Standard for Reinforcement Learning Optimization. Retrieved from https://www.scien.cx/2025/01/14/anc-vi-sets-a-new-standard-for-reinforcement-learning-optimization/
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