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Markov Decision Processes and Value Iteration in Reinforcement Learning
by AnchoringJanuary 14th, 2025
Too Long; Didn't Read
This section reviews key concepts in Markov Decision Processes (MDP) and reinforcement learning, covering notations, value iteration, and Bellman operators for optimal policy calculation.
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.
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
1.1 Notations and preliminaries
We quickly review basic definitions and concepts of Markov decision processes (MDP) and reinforcement learning (RL). For further details, refer to standard references such as [69, 81, 84].
This paper is available on arxiv under CC BY 4.0 DEED license.
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