Aim Low: Correlation-based Feature Selection for Model-based Reinforcement Learning
نویسندگان
چکیده
We explored a series of feature selection methods for modelbased Reinforcement Learning (RL). More specifically, we explored four common correlation metrics and based on them, we proposed the fifth one named Weighed Information Gain (WIG). While much existing correlation-based feature selection methods mostly explored high correlation by default, we explored two options: High vs. Low. The former selects the next feature that has the highest correlation measure with existing selected ones while the latter selects the one with the lowest correlations. The 10 correlation-based methods were compared against previous feature selection methods for model-based RL across several datasets collected from two vastly different intelligent tutoring systems. Our results showed that the 10 correlation-based methods significantly outperform all other methods across all datasets. Among the five correlation metrics, WIG performed best. Surprisingly, for each of correlation metrics, the low option significantly outperform its high correlation peer and thus it suggests that low correlation-based feature selection methods are more effective for model-based RL than high ones.
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