This formulation of the mining problem as an MDP enables the generalization of the approach to other problems that can be modeled under the premises of states, actions, and rewards. Essentially, any problem that can be described using these elements is amenable to being solved through Reinforcement Learning techniques. 3. CASE OF STUDY This section presents a detailed case study in the mining sector, focusing on the application of advanced data analysis techniques and machine learning algorithms to optimize mineral resource exploitation. The main objective is to maximize the Net Present Value (NPV) of the project, considering the operational and economic constraints of the mining environment. Using Reinforcement Learning approach, the goal is to develop production strategies that are not only economically viable but also technically feasible, enabling more efficient management of mineral resources. In Figure 4 it can be see the relationship between cut off grade, tonnage and average grade of resources. Also, in Figure 5 it can be see the economic parameters of mining operations that include processing, mining cost, prices of commodities and other parameters that permit evaluated in economic terms the feasibility of the project. Figure 4 - Geology Resources In Table 2 we can see the economics parameters also in Table 3 we can see the operating parameters such processing and mining capacities in terms of tonnage of rock by year. Table 2 - Economic Parameters Parameter Value Unit Mineral Price 0.90 USD/lb
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