A dimensionality reduction approach for many-objective Markov Decision Processes: Application to a water reservoir operation problem
We present a dimensionality reduction approach for many-objective Markov Decision Processes. A lower dimensional representation of the original objectives is identified via Non-negative Principal Component Analysis. The approach is demonstrated on the operation of a multi-purpose water reservoir (Tono Dam, Japan) involving 10 objectives. The NPCA-based solutions provide a good representation of the 10 objective Pareto front in terms of consistency and diversity.
markov decision processes non-negative principal component analysis many-objective optimization visual analytics water resources management