What is it about?
Markov decision process is a dynamic programming algorithm that can be used to solve an optimization problem. It was used in applications like robotics, radar tracking, medical treatments, and decision-making. In the existing literature, the researcher only targets a few applications area of MDP. However, this work surveyed the Markov decision process’s application in various regions for solving optimization problems.
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Why is it important?
In a survey, we compared optimization techniques based on MDP. We performed a comparative analysis of past work of other researchers in the last few years based on a few parameters. These parameters are focused on the proposed problem, the proposed methodology for solving an optimization problem, and the results and outcomes of the optimization technique in solving a specific problem.
Perspectives
Reinforcement learning is an emerging machine learning domain based on the Markov decision process. In this work, we conclude that the MDP-based approach is most widely used when deciding on the current state in some environments to move to the next state.
Richard (Ricky) Smith Jr.
Read the Original
This page is a summary of: Exploring Markov Decision Processes: A Comprehensive Survey of Optimization Applications and Techniques, IgMin Research, July 2024, IgMin Publications Inc.,
DOI: 10.61927/igmin210.
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