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Temporal difference learning with interpolated table value functions

Lucas, SM (2009) Temporal difference learning with interpolated table value functions. In: UNSPECIFIED, ? - ?.


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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 themountain car problem, a standard reinforcement learning benchmark. In each case, the interpolated table functions offer competitive performance. ©2009 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: CIG2009 - 2009 IEEE Symposium on Computational Intelligence and Games
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 11 Oct 2012 12:58
Last Modified: 23 Jan 2019 02:15

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