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ESANN
2004

High-accuracy value-function approximation with neural networks applied to the acrobot

9 years 11 months ago
High-accuracy value-function approximation with neural networks applied to the acrobot
Several reinforcement-learning techniques have already been applied to the Acrobot control problem, using linear function approximators to estimate the value function. In this paper, we present experimental results obtained by using a feedforward neural network instead. The learning algorithm used was model-based continuous TD(). It generated an efficient controller, producing a high-accuracy state-value function. A striking feature of this value function is a very sharp 4-dimensional ridge that is extremely hard to evaluate with linear parametric approximators. From a broader point of view, this experimental success demonstrates some of the qualities of feedforward neural networks in comparison with linear approximators in reinforcement learning.
Rémi Coulom
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where ESANN
Authors Rémi Coulom
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