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CIG
2006
IEEE
14 years 2 months ago
Monte-Carlo Go Reinforcement Learning Experiments
Abstract— This paper describes experiments using reinforcement learning techniques to compute pattern urgencies used during simulations performed in a Monte-Carlo Go architecture...
Bruno Bouzy, Guillaume Chaslot
ICML
2009
IEEE
14 years 9 months ago
Monte-Carlo simulation balancing
In this paper we introduce the first algorithms for efficiently learning a simulation policy for Monte-Carlo search. Our main idea is to optimise the balance of a simulation polic...
David Silver, Gerald Tesauro
ICML
2000
IEEE
14 years 9 months ago
Eligibility Traces for Off-Policy Policy Evaluation
Eligibility traces have been shown to speed reinforcement learning, to make it more robust to hidden states, and to provide a link between Monte Carlo and temporal-difference meth...
Doina Precup, Richard S. Sutton, Satinder P. Singh
ICRA
2009
IEEE
138views Robotics» more  ICRA 2009»
14 years 3 months ago
Which landmark is useful? Learning selection policies for navigation in unknown environments
Abstract— In general, a mobile robot that operates in unknown environments has to maintain a map and has to determine its own location given the map. This introduces significant...
Hauke Strasdat, Cyrill Stachniss, Wolfram Burgard
ACG
2003
Springer
14 years 1 months ago
Evaluation in Go by a Neural Network using Soft Segmentation
In this article a neural network architecture is presented that is able to build a soft segmentation of a two-dimensional input. This network architecture is applied to position ev...
Markus Enzenberger