Comparing Learning Classifier System and Reinforcement Learning with Function Approximation
Atsushi Wada, Keiki Takadama, Katsunori Shimohara, Osamu Katai · IEEJ Transactions on Electronics Information and Systems · 2004
As a first step toward an analysis of the capabilities of adaptive systems, including learning and evolution, we focus on the Learning Classifier System (LCS) and compare it with Reinforcement Learning (RL) that adopts the Function Approximation (FA) method. An analysis of this comparison found an equivalence of learning processes between both the two models, which brings the mathematical framework of the LCS’s learning process to the level of RL with FA. Our analysis also clarified the limitations of the results.