Multi-objectivization and ensembles of shapings in reinforcement learning.

Ensemble techniques are a powerful approach to creating better decision makers in machine learning. Multiple decision makers are trained to solve a given task, grouped in an ensemble, and their decisions are aggregated. The ensemble derives its power from the diversity of its components, as the assumption is that they make mistakes on different inputs, and that the majority is more likely to be correct than any individual component. Diversity usually comes from the different algorithms employed by the decision makers, or the different inputs used to train the decision makers.