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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.

Global Versus Local Constructive Function Approximation for On-Line Reinforcement Learning