## 4 Framework Proposal
In general, there are two common approaches to achieving this: applying optimization techniques to predicted values or utilizing simulation techniques to derive the best decisions directly from the data. Therefore, in a formal sense, prescriptive analytics comprises two fundamental components. The first component involves framing a decision problem, while the second component pertains to utilizing noisy observations from the environment, which only offer an incomplete indication of the true state of a system. The objective of prescriptive analytics is to use the available data to recommend the optimal decision, considering the limitations imposed by the noisy observations. PsM techniques are good examples of sequential decision problems under uncertainty, implying that the overall outcome depends on a sequence of decisions. MDPs provide a powerful analytical framework for such settings and are often used to represent these problems [65]. An MDP framework has the following key components:  
* S--states (\(s\in S\));
* A--actions (\(a\in A\));
* P--transition probabilities (\(P(S_{t+1}|S_{t})\));
* R--reward (\(R(S_{t},A)\)). A graphical representation of the MDP model can be seen in Figure 4. According to this scheme and depending on the specific decision-making process in question, the policy to be enforced needs to be established, and the RL optimization agent will explore the relevant set of state sequences depending on the environment. In the context of RL, the environment plays a crucial role, as it represents a model of the world where an agent operates. The environment is responsible for specifying the state space. In addition, it controls the consequences of actions, including state transitions and rewards, and relays this information to the agent. In typical baseline environments, state transitions and rewards are often assumed to be independent of external factors such as strain or weather conditions. However, real-world applications often require the incorporation of these effects. In maintenance management, the environment plays a critical role as an information hub, integrating data from the condition-monitoring system, which captures the natural degradation of the system. This is precisely where the set of models developed and maintained using the framework proposed in this paper becomes valuable. This set of models can help account for the environment by improving the estimation of rewards for a range of exploratory actions that the agent needs to explore. Although these actions are determined based on the policy adopted and the current status of an asset, the predicted behavior estimated by the set of models is crucial to determining the reward associated with each action. In order to identify the optimal action, it is crucial to establish the returns, which represent the rewards received by the agent in each state. In this regard, the horizon method, which exclusively emphasizes short-term or long-term rewards, is  
Figure 4: Markov Decision Process (MDP) model. not preferred. Instead, a variable called the \"discount factor (\(\gamma\))\" is introduced [66]. This factor determines the weight given to immediate rewards when it is closer to zero, while values closer to one prioritize long-term rewards. Therefore, the discounted infinite-horizon method plays a vital role in determining the best action by appropriately balancing short-term and long-term rewards. Therefore, considering the reward of the current state and the discounted reward value of the next state brings us to the Bellman equation [67]. Furthermore, the reward the environment provides to the agent serves as a proxy for the true cost incurred, but it is subject to uncertainty until the asset is actually maintained. The comprehensive approach provided by the proposed framework can provide recommendations for actions in the decision-making process, and, even better, it can be explained based on the policy imposed and the rewards for the different actions.