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
F Omitted proofs in Section 6
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\ Next, we prove following key lemma
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\ Proof of Lemma 21. First, we prove first inequality in Lemma 21 by induction.
\ If k= 0,
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\ By induction,
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\ First, we prove second inequality in Lemma 21 by induction.
\ If k= 0,
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\ By induction.
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\ Now, we prove the first rate in Theorem 7.
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\ For the second rates of Theorem 7, we introduce following lemma.
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\ Now, we prove the second rates in Theorem 7.
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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 (2025-01-15T22:00:06+00:00) Making Sense of AI Learning Proofs. Retrieved from https://www.scien.cx/2025/01/15/making-sense-of-ai-learning-proofs/
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