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Temporal difference learning with interpolated table value functions

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1 Author(s)
Lucas, S.M. ; Sch. of Comput. Sci. & Electron. Eng., Univ. of Essex, Colchester, UK

This paper introduces a novel function approximation architecture especially well suited to temporal difference learning. The architecture is based on using sets of interpolated table look-up functions. These offer rapid and stable learning, and are efficient when the number of inputs is small. An empirical investigation is conducted to test their performance on a supervised learning task, and on the mountain car problem, a standard reinforcement learning benchmark. In each case, the interpolated table functions offer competitive performance.

Published in:

Computational Intelligence and Games, 2009. CIG 2009. IEEE Symposium on

Date of Conference:

7-10 Sept. 2009

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