Comparison of Statistical Table- and Non-Statistical Table-based XCS in Noisy Environments
Takato Tatsumi, Keiki Takadama · 2019
Accuracy based Learning Classifier System (XCS) acquires generalized classifiers that can guess the appropriate output for all inputs with a small number of the classifiers in ideal environments where there is no uncertainty in inputs, outputs, and rewards. However, if uncertainty is included in any of inputs, outputs, and rewards, XCS cannot be properly learned and cannot stably acquire generalized classifiers. We proposed Learning Classifier Systems that can properly learn in environments to which specific noise is added. These methods are divided into two types: (i) statistical table based XCS that record the mean of rewards acquired in all input-output pairs, and (ii) non-statistical table based XCS that do not record their values. This paper applies these methods to multiple noise environments and clarifies the features of each method.