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AUTOMATICA
2008
92views more  AUTOMATICA 2008»
14 years 9 months ago
Box particle filtering for nonlinear state estimation using interval analysis
In recent years particle ...lters have been applied to a variety of state estimation problems. A particle ...lter is a sequential Monte Carlo Bayesian estimator of the posterior d...
Fahed Abdallah, Amadou Gning, Philippe Bonnifait
ECML
2004
Springer
15 years 3 months ago
Filtered Reinforcement Learning
Reinforcement learning (RL) algorithms attempt to assign the credit for rewards to the actions that contributed to the reward. Thus far, credit assignment has been done in one of t...
Douglas Aberdeen

Publication
154views
13 years 12 months ago
Preference elicitation and inverse reinforcement learning
We state the problem of inverse reinforcement learning in terms of preference elicitation, resulting in a principled (Bayesian) statistical formulation. This generalises previous w...
Constantin Rothkopf, Christos Dimitrakakis
ICMCS
2007
IEEE
191views Multimedia» more  ICMCS 2007»
15 years 3 months ago
Variable Number of "Informative" Particles for Object Tracking
Particle filter is a sequential Monte Carlo method for object tracking in a recursive Bayesian filtering framework. The efficiency and accuracy of the particle filter depends on t...
Yu Huang, Joan Llach
GECCO
2006
Springer
168views Optimization» more  GECCO 2006»
15 years 1 months ago
A Bayesian approach to learning classifier systems in uncertain environments
In this paper we propose a Bayesian framework for XCS [9], called BXCS. Following [4], we use probability distributions to represent the uncertainty over the classifier estimates ...
Davide Aliprandi, Alex Mancastroppa, Matteo Matteu...