The cascade-correlation learning architecture
Advances in neural information processing systems 2
Reinforcement learning for robots using neural networks
Reinforcement learning for robots using neural networks
Temporal difference learning and TD-Gammon
Communications of the ACM
Investigation of the CasCor family of learning algorithms
Neural Networks
Learning to Predict by the Methods of Temporal Differences
Machine Learning
| Hi-index | 0.00 |
In order to scale to problems with large or continuous state-spaces, reinforcement learning algorithms need to use function approximation. Neural networks are one commonly used approach, with most work so far using fixed-architecture networks. Previous supervised learning research has shown that constructive networks which grow their architecture during training outperform fixed-architecture networks. This paper extends the sarsa algorithm to use a cascade constructive network, and shows it outperforms a fixed-architecture network on two benchmark tasks.