Breaking down complex concepts in reinforcement learning

:::information
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
D- Evidence omitted in Section 4
We present the proof of Theorem 5.
\
\
\
\
:::Information of this paper Available on arXiv Under CC BY 4.0 DEED license.
:::
\