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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Nakata, M. Lanzi, P.L. Kovacs, T. Browne, W.N. Takadama, K. |
| Copyright Year | 2015 |
| Description | Author affiliation: Univ. of Bristol, Bristol, UK (Kovacs, T.) || Politec. di Milano, Milan, Italy (Lanzi, P.L.) || Univ. of Electo-Commun., Tokyo, Japan (Nakata, M.; Takadama, K.) || Victoria Univ. of Wellington, Wellington, New Zealand (Browne, W.N.) |
| Abstract | A learning strategy in Learning Classifier Systems (LCSs) defines how classifiers cover a state-action space in a problem. Previous analyses in classification problems have empirically claimed an adequate learning strategy can be decided depending on the types of noise in the problem. This issue is still arguable from two aspects. First, there lacks comparison of learning strategies in reinforcement learning problems with different types of noise. Second, when we can claim so, a further issue is how should classifiers cover the state-action space in order to improve the stability of LCS performance on as many types of noise as possible? This paper first attempts to empirically conclude these issues on a version of LCSs (i.e., the XCS classifier system). That is, we present a new concept of learning strategy for LCSs, and complement that claim by comparing it with the existing learning strategies on a reinforcement learning problem. Our learning strategy covers all state-action pairs but assigns more classifiers to the highest-return action at each state than other actions. Our results support that claim that existing learning strategies have dependencies on the types of noise in reinforcement learning problems. However, our learning strategy improves the stability of XCS performance compared with the existing strategies on all types of noise employed in this paper. |
| Starting Page | 3012 |
| Ending Page | 3019 |
| File Size | 571151 |
| Page Count | 8 |
| File Format | |
| e-ISBN | 9781479974924 |
| DOI | 10.1109/CEC.2015.7257264 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-05-25 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Learning (artificial intelligence) Supervised learning Gaussian noise Guidelines Mathematical model Sociology reinforcement learning Learning Classifier System XCS complete action map best action map |
| Content Type | Text |
| Resource Type | Article |
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