Provably efficient learning with typed parametric models
To quickly achieve good performance, reinforcement-learning algorithms for acting in large continuous-valued domains must use a representation that is both sufficiently powerful to capture important domain characteristics, and yet simultaneously allows generalization, or sharing, among experiences....
| Main Authors: | , , , , |
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| Other Authors: | , |
| Format: | Article |
| Language: | English |
| Published: |
Journal of Machine Learning Research,
2010-11-29T17:59:03Z.
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| Subjects: | |
| Online Access: | Get fulltext |