Anticipatory Classifier System with Average Reward Criterion in Discretized Multi-Step Environments

Initially, Anticipatory Classifier Systems (ACS) were designed to address both single and multistep decision problems. In the latter case, the objective was to maximize the total discounted rewards, usually based on Q-learning algorithms. Studies on other Learning Classifier Systems (LCS) revealed many real-world sequential decision problems where the preferred objective is the maximization of the average of successive rewards. This paper proposes a relevant modification toward the learning component, allowing us to address such problems. The modified system is called AACS2 (Averaged ACS2) and is tested on three multistep benchmark problems.