A comparison of Q-learning and classifier systems
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Manufacturing processes need their entities to coordinate and discoordinate at different times of the process, in order to achieve the adequate manufacturing timing some jobs need to be done in a sequence or can be done in parallel, using different machines. This paper introduces a complex discoordination problem: the multibar problem, based on the ''El Farol'' bar problem, devised to test enhanced complexity for multi-agent systems. A multi-agent system that learns based on the extended classifier system (MAXCS) is used for the simulation. Different classifier population sizes are used to help agent adaptation. MAXCS adapts to the all possible configurations of the different bars tested in 20 different experiments in different ways. The first set of experiments proved that MAXCS is able to adapt to the multibar problem with the emergence of several agents switching bars (vacillating agents). The preliminary experiments yielded the hypothesis of the irrelevance of the classifiers' rule conditions, and their evolution to influence the result. MAXCS is then compared with multi-agent Q-learning (MAQL). These experiments demonstrate the need to use evolutionary computation for better adaptation, rather than just a reinforcement learning algorithm, proving wrong the previous hypothesis. The MAXCS-MAQL comparison showed that the use of rule conditions, combined with the genetic algorithm, determines whether there is only one or several vacillating agents at the same time throughout the experiment. The solution scales when 133 agents are used for the problem. After this study, it can be concluded that the multibar problem can become an interesting benchmark for multi-agent learning and provide manufacturing processes with suitable coordination solutions.