| URI | http://purl.tuc.gr/dl/dias/B95FD666-3683-44DB-8681-8CB3C2DFEC7B | - |
| Language | en | - |
| Extent | 3 pages | en |
| Title | Approximate policy iteration using large-margin classifiers | en |
| Creator | Lagoudakis Michael | en |
| Creator | Λαγουδακης Μιχαηλ | el |
| Content Summary | We present an approximate policy iteration algorithm
that uses rollouts to estimate the value of each
action under a given policy in a subset of states and
a classifier to generalize and learn the improved
policy over the entire state space. Using a multiclass
support vector machine as the classifier, we
obtained successful results on the inverted pendulum
and the bicycle balancing and riding domains. | en |
| Type of Item | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Full Paper | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2015-11-13 | - |
| Date of Publication | 2003 | - |
| Subject | Artificial Intelligence | en |
| Bibliographic Citation | M.G. Lagoudakis and R. Parr, “Approximate policy iteration using large-margin classifiers,” in Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI), 2003, pp. 1432–1434. | en |