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Unpacking the main arguments in reinforcement learning

Authors:

(1) Jongmin Lee, Department of Mathematical Sciences, Seoul National University;

(2) Ernest K. Ryu, Department of Mathematical Sciences, Seoul National University and Interdisciplinary Program in Artificial Intelligence, Seoul National University.

One summary and introduction

1.1 Notes and introductions

1.2 Previous works

2 Repeat the vertical value

2.1 Accelerating rate of Bellman consistency operator

2.2 The accelerating rate of Bellman’s ideal opera

3 Convergence when y=1

4 Minimum complexity

5 Repeat the approximate vertical value

6 Gauss-Seidel iteration of the established value

7 Conclusion, acknowledgments, funding disclosure, and references

A preliminary

B- Evidence omitted in Section 2

C- Evidence omitted in Section 3

D- Evidence omitted in Section 4

E – Evidence omitted in Section 5

F- Evidence omitted in Section 6

Wider impacts

h restrictions

C- Evidence omitted in Section 3

First, we introduce the following lemma.

Where the second inequality comes from the non-expansion of T.

Now we present the proof of the third theorem.

Next, we prove Theorem 4.

This paper is available on arxiv under a CC BY 4.0 DEED license.

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