The cascade-correlation learning architecture
Advances in neural information processing systems 2
A resource-allocating network for function interpolation
Neural Computation
Temporal difference learning and TD-Gammon
Communications of the ACM
Investigation of the CasCor family of learning algorithms
Neural Networks
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Q-Learning with Hidden-Unit Restarting
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Feedforward Neural Networks in Reinforcement Learning Applied to High-Dimensional Motor Control
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
The cascade-correlation learning: a projection pursuit learning perspective
IEEE Transactions on Neural Networks
A reinforcement learning framework for online data migration in hierarchical storage systems
The Journal of Supercomputing
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In order to scale to large state-spaces, reinforcement learning (RL) algorithms need to apply function approximation techniques. Research on function approximation for RL has so far focused either on global methods with a static structure or on constructive architectures using locally responsive units. The former, whilst achieving some notable successes, has also failed on some relatively simple tasks. The locally constructive approach is more stable, but may scale poorly to higher-dimensional inputs. This paper examines two globally constructive algorithms based on the Cascor supervised-learning algorithm. These algorithms are applied within the sarsa RL algorithm, and their performance compared against a multi-layer perceptron and a locally constructive algorithm (the Resource Allocating Network). It is shown that the globally constructive algorithms are less stable, but that on some tasks they achieve similar performance to the RAN, whilst generating more compact solutions.