Approximating Value Functions in Classifier Systems

While there has been some attention given recently to the issues of function approximation using learning classifier systems (e.g. [13, 3]), few studies have looked at the quality of the value function approximation computed by a learning classifier system when it solves a reinforcement learning problem [1, 8]. By contrast, considerable attention has been paid to this issue in the reinforcement learning literature [12]. One of the fundamental assumptions underlying algorithms for solving reinforcement learning problems is that states and state-action pairs have well-defined values that can be computed and used to help determine an optimal policy. The quality of those approximations is a critical factor in determining the success of many algorithms in solving reinforcement learning problems.