269 Figure 2 - Overview of Reinforcement Learning Figure 3 - Topology Markov Decision Process Reinforcement Learning offers several advantages over traditional methods. First, it can explore multiple potential solutions instead of converging on a single solution, preserving diversity in the solution space. Second, it incorporates a mechanism of continuous learning, allowing the system to improve its performance over time through accumulated experience. Third, it dynamically adapts its policies in response to parameter changes, eliminating the need for full re-optimization. The mining problem used as an example can be more generally and technically conceptualized through the framework of Reinforcement Learning (RL). This approach allows modeling the problem as a sequential decision-making game, where each system state, available actions, and obtained rewards are considered within a formal structure called a Markov Decision Process (MDP). We can decompose the Mining Problem into an MDP:
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