Abstract
A method is proposed for situation-dependent input selection and learning acceleration in Q-learning. Q-values are expressed by an RBF network which has an input gate attached to each of its input channels in order to capture, by learning, situation-dependent relevance or usefulness of the input.
| Original language | English |
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| Pages | 5-10 |
| Number of pages | 6 |
| Publication status | Published - 2002 |
| Event | 2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States Duration: May 12 2002 → May 17 2002 |
Other
| Other | 2002 International Joint Conference on Neural Networks (IJCNN '02) |
|---|---|
| Country/Territory | United States |
| City | Honolulu, HI |
| Period | 5/12/02 → 5/17/02 |
All Science Journal Classification (ASJC) codes
- Software
- Artificial Intelligence