Complete action map or best action map in accuracy-based reinforcement learning classifier systems

Abstract

We study two existing Learning Classifier Systems (LCSs): XCS, which has a complete map (which covers all actions in each state), and XCSAMm, which has a best action map (which covers only the highest-return action in each state). This allows XCSAM to learn with a smaller population size limit (but larger population size) and to learn faster than XCS on well-behaved tasks. However, many tasks have dif- ficulties like noise and class imbalances. XCS and XCSAM have not been compared on such problems before. This pa- per aims to discover which kind of map is more robust to these difficulties. We apply them to a classification problem (the multiplexer problem) with class imbalance, Gaussian noise or alternating noise (where we return the reward for a different action). We also compare them on real-world data from the UCI repository without adding noise. We analyze how XCSAM focuses on the best action map and introduce a novel deletion mechanism that helps to evolve classifiers towards a best action map. Results show the best action map is more robust (has higher accuracy and sometimes learns faster) in all cases except small amounts of alternat- ing noise.