Abstract
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.
Highlights
We introduce the average reward criterion to yet another family of
The following section describes the differences observed between using the ACS2 with standard discounted reward distribution and two proposed modifications
The following section describes the differences observed between using the ACS2 with standardIn all cases, the experiments were performed in an explore–exploit manner, where the mode was discounted reward distribution and two proposed modifications
Summary
Anticipatory Classifier System withAverage Reward Criterion in Discretized Multi-Step Environments.Appl.
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