∈Ω = max ∈Ω ( , +( 1− 1+ )(1 + ∑ −1, (1+ ) − = +1 ) Δ Δ ) (3) ∗( , ) ≅ ∈Ω (4) 2.1 Reinforcement Learning in Non-Renewable Resources The optimization of non-renewable resources, such as mining, is a significant challenge for both industry and academia. Traditional approaches, using algorithm that implement MILP or Metaheuristics, have proven effective in finding optimal solutions under given initial conditions. However, these methods exhibit several limitations, including the generation of a single solution, lack of adaptive learning, the need for re-optimization with parameter changes, and uncertainty in the success probability of the obtained solution. In this context, we propose an abstraction of mining problems using Reinforcement Learning (RL). Mining problems can be naturally modeled as reinforcement learning games, where periods represent states, extraction decisions correspond to actions, and the cash flow of the mining model serves as the reward like we can show in Figure 2 and Figure 3. This approach addresses the limitations of classical optimization methods, providing a more flexible and adaptive framework.
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