A dimensionality reduction approach for many-objective Markov Decision Processes: Application to a water reservoir operation problem

Abstract The operation of complex environmental systems usually accounts for multiple, conflicting objectives, whose presence imposes to explicitly consider the preference structure of the parties involved. Multi-objective Markov Decision Processes are a useful mathematical framework for the resolution of such sequential, decision-making problems. However, the computational requirements of the available optimization techniques limit their application to problems involving few objectives. In real-world applications it is therefore common practice to select few, representative objectives with respect to which the problem is solved. This paper proposes a dimensionality reduction approach, based on the Non-negative Principal Component Analysis (NPCA), to aggregate the original objectives into a reduced number of principal components, with respect to which the optimization problem is solved. The approach is evaluated on the daily operation of a multi-purpose water reservoir (Tono Dam, Japan) with 10 operating objectives, and compared against a 5-objectives formulation of the same problem. Results show that the NPCA-based approach provides a better representation of the Pareto front, especially in terms of consistency and solution diversity.