  As a genetics-based machine learning technique, zeroth-level classifier
system (ZCS) is based on a discounted reward reinforcement learning algorithm,
bucket-brigade algorithm, which optimizes the discounted total reward received
by an agent but is not suitable for all multi-step problems, especially
large-size ones. There are some undiscounted reinforcement learning methods
available, such as R-learning, which optimize the average reward per time step.
In this paper, R-learning is used as the reinforcement learning employed by
ZCS, to replace its discounted reward reinforcement learning approach, and
tournament selection is used to replace roulette wheel selection in ZCS. The
modification results in classifier systems that can support long action chains,
and thus is able to solve large multi-step problems.
