Name
Abstract | ||
|---|---|---|
Exploration/Exploitation equilibrium is one of the most challenging issues in reinforcement learning area as well as learning classifier systems such as XCS. In this paper, an intelligent method is proposed to control exploration rate in XCS to improve its long-term performance. This method is called Intelligent Exploration Method (IEM) and is applied to some benchmark problems to show advantages of adaptive exploration rate for XCS. It |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1145/1102256.1102279 | GECCO Workshops |
Keywords | Field | DocType |
intelligent exploration method,long-term performance,challenging issue,adaptive exploration rate,intelligent method,exploration rate,benchmark problem,classifier system,exploitation equilibrium,exploration,learning classifier system,reinforcement learning | Computer science,Artificial intelligence,Classifier (linguistics),Machine learning,Reinforcement learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali Hamzeh | 1 | 214 | 29.47 |
| Adel Torkaman Rahmani | 2 | 139 | 19.77 |