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Conference Papers Year : 2008

Basis Function Construction in Reinforcement Learning using Cascade-Correlation Learning Architecture

Abstract

In reinforcement learning, it is a common practice to map the state(-action) space to a different one using ba- sis functions. This transformation aims to represent the input data in a more informative form that facilitates and improves subsequent steps. As a "good" set of basis func- tions result in better solutions and defining such functions becomes a challenge with increasing problem complexity, it is beneficial to be able to generate them automatically. In this paper, we propose a new approach based on Bellman residual for constructing basis functions using cascade- correlation learning architecture. We show how this ap- proach can be applied to Least Squares Policy Iteration al- gorithm in order to obtain a better approximation of the value function, and consequently improve the performance of the resulting policies. We also present the effectiveness of the method empirically on some benchmark problems.
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Dates and versions

hal-00826054 , version 1 (27-05-2013)

Identifiers

  • HAL Id : hal-00826054 , version 1

Cite

Sertan Girgin, Philippe Preux. Basis Function Construction in Reinforcement Learning using Cascade-Correlation Learning Architecture. International Conference on Machine Learning and Applications, Dec 2008, San Diego, United States. pp.75-82. ⟨hal-00826054⟩
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