The need for automated decision-making is steadily increasing. Hence, data-driven decision-making techniques are essential. We assume a system that follows certain dynamics and has to be tuned or controlled over time such that certain constraints are satisfied and a specified objective is optimized. Typically, the current state of the system as well as the interplay of rewards and potential future states associated to certain actions have to be taken into account. The dynamics and state transitions may have to be estimated from data using suitable ML-based techniques.
As, in general, exact solution approaches of such dynamic optimization problems do not scale often heuristics have to be used (e.g., in case the number of states is too large, cf. curse of dimensionality). Besides classical approaches such as dynamic programming (DP) state-of-the-art heuristic optimization techniques such as approximate dynamic programming (ADP) or reinforcement learning (RL) are suitable alternatives.
