Using the XCS classifier system for multi-objective reinforcement learning problems
We investigate the performance of a learning classifier system in some simple multi-objective, multi-step maze problems, Using both random and biased action-selection policics for exploration. Results show that the choice of action-selection policy can significantly affect the performance of the system in such environments. Further, this effect is directly related to population size, and we relate this finding to recent theoretical studies of learning classifier systems in sngle-step problems.
population size action selection autonomous agents genetic algorithm