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Learning Classifier Systems (LCSs) are rule-based adaptive systems  that have both Reinforcement Learning (RL) and rule-discovery  mechanisms for effective and practical online learning. An analysis  of the reinforcement process of XCS, one of the currently mainstream  LCSs, is performed from the aspect of RL. Upon comparing XCS's update  method with gradient-descent-based parameter update in RL, differences  are found in the following elements: (1) residual term, (2) gradient  term, and (3) payoff definition. All possible combinations of the  variants in each element are implemented and tested on multi-step  benchmark problems. This revealed that few specific combinations work  effectively with XCS's accuracy-based rule-discovery process, while  pure gradient-descent-based update showed the worst performance.

Is Gradient Descent Update Consistent with Accuracy-Based Learning Classifier System?