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: • States ( ): In this context, a state ∈ at time represents a complete description of the mining environment at a given moment. This includes, among other factors, the remaining resources, geological conditions, available equipment, and any other relevant parameters influencing mining operations. • Actions ( ): Actions ∈ ( ) are operational decisions that the agent (decision-maker) can execute in state . These may include decisions such as the volume of material to extract, the type of technology to use, transportation routes, and other operational strategies like cut off police. Each action influences the transition from the current state to the next. • State Transition Function ( ):The transition function ( +1, +1| , ) defines the probability of transitioning from state ( ) to state ( +1) after executing action ( ). This function captures the stochastic dynamics of the mining environment, incorporating uncertainties such as geological variations, machinery failures, and environmental conditions. • Rewards ( ): The reward function ( , ) assigns a numerical value to the immediate reward obtained after executing action in state .In the mining context, this typically corresponds to the cash flow generated by mining operations, adjusted by operational costs and other economic factors.
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