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Table 2 Average and standard deviation of the best test result during a run and the total cumulative reward during training.

in Efficient Model-Based Exploration
by Marco Wiering, Jürgen Schmidhuber 1998
"... In PAGE 5: ... First of all, Table 1 shows that this strategy nds opti- mal or near-optimal policies in 90% of the cases, whereas the others fail in at least 50% of the cases. The second improvement with MBIE is shown in Table2 . MBIE col- lects much more reward during training than all other exploration methods, thereby e ectively addressing the exploration/exploitation dilemma.... ..."

Table 3: Coefficient p decides the balance between exploration and exploitation. (Pure

in Exploration exploitation in go: UCT for Monte-Carlo go
by Sylvain Gelly, Yizao Wang 2006
Cited by 3

Table 3: Coefficient p decides the balance between exploration and exploitation. (Pure

in Exploration exploitation in go: UCT for Monte-Carlo go
by Sylvain Gelly, Yizao Wang 2006
Cited by 3

Table 6: Coefficient p decides the balance between exploration and exploitation. (Pure

in Modification of UCT with patterns in Monte-Carlo go
by Sylvain Gelly, Yizao Wang, Rémi Munos, Olivier Teytaud 2006

Table 1: The discursive dilemma

in Unanimity consistency in model-based belief merging ∗
by Gabriella Pigozzi, Daniel Eckert 2006

Table h part of the DILEMMA dictionary

in Dilemma-2: A Lemmatizer-Tagger For Medical Abstracts
by Hans Paulussen Facult, Hans Paulussen, Willy Martin 1992
Cited by 5

Table 1: Prisoner dilemma payoff

in Adaptive Behaviour
by For Prisoner Dilemma

Table 2:DILEMMA-1 output sample

in Dilemma-2: A Lemmatizer-Tagger For Medical Abstracts
by Hans Paulussen Facult, Hans Paulussen, Willy Martin 1992
Cited by 5

Table 1: The Payoff Table of the Prisoners Dilemma

in Feature Article
by Edward P. K. Tsang, Serafin Martinez-jaramillo

Table 1. Categorization of different learning tasks with regard to the stability- plasticity dilemma.

in Radial Basis Function Networks 1: Recent Developments in Theory and Applications.
by unknown authors
"... In PAGE 1: ... In the past many learning methods have been proposed which also apply to RBF networks. Table1 aims to bring some order into the terminology, especially from the viewpoint of the stability-plasticity dilemma. Classical RBF learn- ing has dealt with a stationary environment.... ..."
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