Name
Abstract | ||
|---|---|---|
This paper presents a novel hybrid learning method and performance evaluation methodology for adaptive autonomous agents. Measuring the performance of a learning agent is not a trivial task and generally requires long simulations as well as knowledge about the domain. A generic evaluation methodology has been developed to precisely evaluate the performance of policy estimation techniques. This methodology has been integrated into a hybrid learning algorithm which aim is to decrease the learning time and the amount of errors of an adaptive agent. The hybrid learning method namely K-learning, integrates the Q-learning and K Nearest-Neighbors algorithm. Experiments show that the K-learning algorithm surpasses the Q-learning algorithm in terms of convergence speed to a good policy. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11874850_31 | IBERAMIA-SBIA |
Keywords | Field | DocType |
hybrid learning algorithm,adaptive autonomous agent,hybrid learning strategy,q-learning algorithm,adaptive agent,hybrid learning method,performance evaluation methodology,novel hybrid,generic evaluation methodology,k-learning algorithm,k nearest-neighbors algorithm,autonomous agent,k nearest neighbor | Intelligent agent,Autonomous agent,Instance-based learning,Stability (learning theory),Active learning (machine learning),Computer science,Wake-sleep algorithm,Unsupervised learning,Artificial intelligence,Leabra,Machine learning | Conference |
Volume | ISSN | ISBN |
4140 | 0302-9743 | 3-540-45462-4 |
Citations | PageRank | References |
7 | 0.59 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Richardson Ribeiro | 1 | 43 | 11.12 |
| Fabrício Enembreck | 2 | 274 | 38.42 |
| Alessandro L. Koerich | 3 | 525 | 39.59 |