Comparison of CMACs and radial basis functions for local function approximators in reinforcement learning
CMACs and radial basis functions are often used in reinforcement learning to learn value function approximations having local generalization properties. We examine the similarities and differences between CMACs, RBFs and normalized RBFs and compare the performance of Q-learning with each representat...
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| Published in | 1997 IEEE International Conference on Neural Networks Vol. 2; pp. 834 - 837 vol.2 |
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| Main Authors | , |
| Format | Conference Proceeding |
| Language | English |
| Published |
IEEE
1997
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| Subjects | |
| Online Access | Get full text |
| ISBN | 0780341228 9780780341227 |
| DOI | 10.1109/ICNN.1997.616132 |
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| Abstract | CMACs and radial basis functions are often used in reinforcement learning to learn value function approximations having local generalization properties. We examine the similarities and differences between CMACs, RBFs and normalized RBFs and compare the performance of Q-learning with each representation applied to the mountain car problem. We discuss ongoing research efforts to exploit the flexibility of adaptive units to better represent the local characteristics of the state space. |
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| AbstractList | CMACs and radial basis functions are often used in reinforcement learning to learn value function approximations having local generalization properties. We examine the similarities and differences between CMACs, RBFs and normalized RBFs and compare the performance of Q-learning with each representation applied to the mountain car problem. We discuss ongoing research efforts to exploit the flexibility of adaptive units to better represent the local characteristics of the state space. |
| Author | Anderson, C.W. Kretchmar, R.M. |
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| Snippet | CMACs and radial basis functions are often used in reinforcement learning to learn value function approximations having local generalization properties. We... |
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| SubjectTerms | Adaptive control Computer science Function approximation Learning Programmable control Shape State-space methods Table lookup Tiles |
| Title | Comparison of CMACs and radial basis functions for local function approximators in reinforcement learning |
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