How Approximate Anchored Value Iteration Handles Errors in Decision-Making Models

Approximate Anchored Value Iteration (Apx-Anc-VI) is shown to be robust against Bellman operator evaluation errors, offering performance comparable to standard Approximate VI.


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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Abstract and 1 Introduction

1.1 Notations and preliminaries

1.2 Prior works

2 Anchored Value Iteration

2.1 Accelerated rate for Bellman consistency operator

2.2 Accelerated rate for Bellman optimality opera

3 Convergence when y=1

4 Complexity lower bound

5 Approximate Anchored Value Iteration

6 Gauss–Seidel Anchored Value Iteration

7 Conclusion, Acknowledgments and Disclosure of Funding and References

A Preliminaries

B Omitted proofs in Section 2

C Omitted proofs in Section 3

D Omitted proofs in Section 4

E Omitted proofs in Section 5

F Omitted proofs in Section 6

G Broader Impacts

H Limitations

5 Approximate Anchored Value Iteration

In this section, we show that the anchoring mechanism is robust against evaluation errors of the Bellman operator, just as much as the standard approximate VI.

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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


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Anchoring | Sciencx (2025-01-14T22:56:46+00:00) How Approximate Anchored Value Iteration Handles Errors in Decision-Making Models. Retrieved from https://www.scien.cx/2025/01/14/how-approximate-anchored-value-iteration-handles-errors-in-decision-making-models/

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