Please use this identifier to cite or link to this item:
|Type:||Artigo de evento|
|Title:||A Neural Architecture To Address Reinforcement Learning Problems|
|Author:||De Arruda R.L.S.|
Von Zuben F.J.
|Abstract:||In this paper, the Reinforcement Learning problem is formulated equivalently to a Markov Decision Process. We address the solution of such problem using a novel Adaptive Dynamic Programming algorithm which is based on a Multilayer Perceptron Neural Network composed of a parameterized function approximator called Wire-Fitting. Extending such established model, this work makes use of concepts of eligibility to conceive faster learning algorithms. The advantage of the proposed approach is founded on the capability to handle continuous environments and to learn a better policy while following another. Simulation results involving the automatic control of an inverted pendulum are presented to indicate the effectiveness of the proposed algorithm. © 2011 IEEE.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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