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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Bae, J. Chhatbar, P. Francis, J.T. Sanchez, J.C. Principe, J.C. |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Electrical and Computer Engineering, P.O. Box 116130 NEB 486, Bldg #33, University of Florida, Gainesville, FL 32611 USA (Bae, J.; Principe, J.C.) || Department of Biomedical Engineering, University of Miami, Miami, FL 33146 USA (Sanchez, J.C.) || Department of Physiology, SUNY Downstate Medical Center, Brooklyn, NY 11203 USA (Chhatbar, P.; Francis, J.T.) |
| Abstract | This paper introduces a kernel adaptive filter implemented with stochastic gradient on temporal differences, kernel Temporal Difference (TD)(λ), to estimate the state-action value function in reinforcement learning. The case λ=0 will be studied in this paper. Experimental results show the method's applicability for learning motor state decoding during a center-out reaching task performed by a monkey. The results are compared to the implementation of a time delay neural network (TDNN) trained with backpropagation of the temporal difference error. From the experiments, it is observed that kernel TD(0) allows faster convergence and a better solution than the neural network. |
| Starting Page | 5662 |
| Ending Page | 5665 |
| File Size | 576475 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424441211 |
| ISSN | 1557170X |
| e-ISBN | 9781457715891 |
| e-ISBN | 9781424441228 |
| DOI | 10.1109/IEMBS.2011.6091370 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-08-30 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Kernel Decoding Learning Least squares approximation Machine learning algorithms Prosthetics Vectors |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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