In this paper, we propose a series of correlation-based feature selection methods for dealing with high dimensionality in feature-rich environments for modelbased Reinforcement Learning (RL). Real world RL tasks usually involve highdimensional feature spaces where standard RL methods often perform badly. Our proposed approach adopts correlation among state features as a selection criterion. The...
