Aimed at achieving multi-step reinforcement learning in continuous spaces, many Learning Classifier Systems have been developed recently to learn fuzzy logic rules. Among these systems, accuracy-based Michigan learning fuzzy classifier systems are gaining increasing research attention. However, in order to learn effectively, existing accuracy-based systems often require the action space to be discrete.Without this restriction, only single-step learning may be supported. In this paper, we will develop a new accuracy-based learning fuzzy classifier system that can perform multi-step reinforcement learning in completely continuous domains. To achieve this goal, a special fuzzy logic system will be introduced in this paper where the output action from the system is modelled through a continuous probability distribution. A natural gradient learning technique will be further exploited to fine-tune the action outputs of individual fuzzy rules. The effectiveness of our learning system has been verified on several benchmark problems.
CITATION STYLE
Chen, G., Douch, C., Zhang, M., & Pang, S. (2015). Reinforcement learning in continuous spaces by using learning fuzzy classifier systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9490, pp. 320–328). Springer Verlag. https://doi.org/10.1007/978-3-319-26535-3_37
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