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» Reinforcement Learning State Estimator
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ICML
2003
IEEE
16 years 1 months ago
Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning
We present a novel Bayesian approach to the problem of value function estimation in continuous state spaces. We define a probabilistic generative model for the value function by i...
Yaakov Engel, Shie Mannor, Ron Meir
ICASSP
2011
IEEE
14 years 4 months ago
A sliding-window online fast variational sparse Bayesian learning algorithm
In this work a new online learning algorithm that uses automatic relevance determination (ARD) is proposed for fast adaptive nonlinear filtering. A sequential decision rule for i...
Thomas Buchgraber, Dmitriy Shutin, H. Vincent Poor
95
Voted
NIPS
2001
15 years 1 months ago
Linking Motor Learning to Function Approximation: Learning in an Unlearnable Force Field
Reaching movements require the brain to generate motor commands that rely on an internal model of the task's dynamics. Here we consider the errors that subjects make early in...
O. Donchin, Reza Shadmehr
FLAIRS
2004
15 years 1 months ago
Intelligent Control of Closed-Loop Sedation in Simulated ICU Patients
The intensive care unit is a challenging environment to both patient and caregiver. Continued shortages in staffing, principally in nursing, increase risk to patient and healthcar...
Brett L. Moore, Eric D. Sinzinger, Todd M. Quasny,...
104
Voted
NN
2010
Springer
125views Neural Networks» more  NN 2010»
14 years 10 months ago
Parameter-exploring policy gradients
We present a model-free reinforcement learning method for partially observable Markov decision problems. Our method estimates a likelihood gradient by sampling directly in paramet...
Frank Sehnke, Christian Osendorfer, Thomas Rü...