XCS with Computable Prediction in Multistep Environments

XCSF extends the typical concept of learning classifier systems through the introduction of computable classifier prediction. Initial results show that XCSF’s computable prediction can be used to evolve accurate piecewise linear approximations of simple functions. In this paper, we take XCSF one step further and apply it to typical reinforcement learning problems involving delayed rewards. In essence, we use XCSF as a method of generalized (linear) reinforcement learning to evolve piecewise linear approximations of the payoff surfaces of typical multistep problems. Our results show that XCSF can easily evolve optimal and near optimal solutions for problems introduced in the literature to test linear reinforcement learning methods.