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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Notsu, A. Honda, K. Ichihashi, H. |
| Copyright Year | 2011 |
| Description | Author affiliation: Osaka Prefecture University, 1-1 Gakuencho, Nakaku, Sakai, Osaka 599-8531, Japan (Notsu, A.; Honda, K.; Ichihashi, H.) |
| Abstract | We propose a novel action-search particle-filtering algorithm for reinforcement learning processes. This algorithm is designed to perform search domain reduction and heuristic space segmentation. In this method, each action space is divided into several new segments using particles. Appropriate search domain reduction can minimize learning time and enable the recognition of the evolutionary process of learning. In a numerical experiment, the proposed filtering method is applied to a single-pendulum simulation in order to demonstrate the adaptability of this simulation model. |
| File Size | 196705 |
| Starting Page | 1755 |
| Ending Page | 1718 |
| File Format | |
| ISBN | 9781424473151 |
| ISSN | 10987584 |
| e-ISBN | 9781424473175 |
| e-ISBN | 9781424473168 |
| DOI | 10.1109/FUZZY.2011.6007337 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-06-27 |
| Publisher Place | Taiwan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Learning Numerical models Genetic algorithms Adaptation models Approximation methods Markov processes space segmentation reinforcement learning particle-filter |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Artificial Intelligence Theoretical Computer Science Software |
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