Name
Abstract | ||
|---|---|---|
To reduce the learning time of reinforcement learning (RL), hybrid algorithms that combines reinforcement learning with various supervised learning methods have attracted many research interests. However, the global convergence and optimality become one of the main problems for hybrid reinforcement learning algorithms. In this paper, the convergence of a hybrid RL algorithm, which is combined with support vector machines(SVMs)is analyzed theoretically. It is shown that by making use of policy gradient learning and the SVM regression, the hybrid algorithm can easily escape from local optima. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ISDA.2006.268 | ISDA (1) |
Keywords | Field | DocType |
learning artificial intelligence,reinforcement learning,hybrid algorithm,regression analysis,support vector machines,supervised learning,convergence,support vector machine | Stability (learning theory),Semi-supervised learning,Instance-based learning,Pattern recognition,Active learning (machine learning),Computer science,Unsupervised learning,Artificial intelligence,Computational learning theory,Machine learning,Reinforcement learning,Learning classifier system | Conference |
Volume | Issue | ISBN |
1 | null | 0-7695-2528-8 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
5 | ||