12/15/2023 0 Comments Unbounded solution space![]() The optimal initial action can be identified by finding a time horizon so long that data beyond it has no effect on the initial decision. ![]() Often, only the optimality of the initial action is of importance to the decision-maker: once it has been identified, the procedure can be repeated to generate a plan of arbitrary length. Unfortunately, these problems are notoriously difficult to solve, due to their infinite dimensionality. Our rule runs faster than comparable rules and discovers shorter, more efficient solution horizons.Ībstract = "Infinite-horizon non-stationary Markov decision processes provide a general framework to model many real-life decision-making problems, e.g., planning equipment maintenance. If no better action can be found, the candidate action is deemed optimal. Given a candidate policy, the rule uses a mathematical program that searches for other possibly optimal initial actions within the space of feasible truncations. We present such a stopping rule for problems with unbounded rewards. This horizon is known as a solution horizon and can be discovered by considering a series of truncations of the problem until a stopping rule guaranteeing initial decision optimality is satisfied. ![]() Infinite-horizon non-stationary Markov decision processes provide a general framework to model many real-life decision-making problems, e.g., planning equipment maintenance. ![]()
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