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The Markov decision process is a mathematical framework of a discrete-time stochastic process. It assists in decision making for models that are partly random and partly under the control of the decision-maker. By using dynamic programming, optimization challenges are worked out [27]. The Markov decision process contains a list of items, e.g., probabilities of transition, states, decision epochs, costs, and actions. The computation in the Markov decision process is very high concerning the increase in the number of states. These issues can be solved by different algorithms such as linear programming, value iteration algorithms.