Analyzing Strength-Based Classifier System from Reinforcement Learning Perspective
Atsushi Wada* and Keiki Takadama**,***
*National Institute of Information and Communications Technology, 2-2-2 Hikaridai, Seikacho, Sorakugun, Kyoto, Japan
**The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, Japan
***PRESTO, Japan Science and Technology Agency (JST), 4-1-8 Honcho Kawaguchi, Saitama 332-0012, Japan
Learning Classifier Systems (LCSs) are rule-based adaptive systems that have both Reinforcement Learning (RL) and rule-discovery mechanisms for effective and practical on-line learning. With the aim of establishing a common theoretical basis between LCSs and RL algorithms to share each field’s findings, a detailed analysis was performed to compare the learning processes of these two approaches. Based on our previous work on deriving an equivalence between the Zeroth-level Classifier System (ZCS) and Q-learning with Function Approximation (FA), this paper extends the analysis to the influence of actually applying the conditions for this equivalence. Comparative experiments have revealed interesting implications: (1) ZCS’s original parameter, the deduction rate, plays a role in stabilizing the action selection, but (2) from the Reinforcement Learning perspective, such a process inhibits the ability to accurately estimate values for the entire state-action space, thus limiting the performance of ZCS in problems requiring accurate value estimation.
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