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JCP
2007
143views more  JCP 2007»
14 years 9 months ago
Noisy K Best-Paths for Approximate Dynamic Programming with Application to Portfolio Optimization
Abstract— We describe a general method to transform a non-Markovian sequential decision problem into a supervised learning problem using a K-bestpaths algorithm. We consider an a...
Nicolas Chapados, Yoshua Bengio
PAMI
2007
186views more  PAMI 2007»
14 years 9 months ago
Value-Directed Human Behavior Analysis from Video Using Partially Observable Markov Decision Processes
—This paper presents a method for learning decision theoretic models of human behaviors from video data. Our system learns relationships between the movements of a person, the co...
Jesse Hoey, James J. Little
136
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PKDD
2010
Springer
164views Data Mining» more  PKDD 2010»
14 years 7 months ago
Efficient Planning in Large POMDPs through Policy Graph Based Factorized Approximations
Partially observable Markov decision processes (POMDPs) are widely used for planning under uncertainty. In many applications, the huge size of the POMDP state space makes straightf...
Joni Pajarinen, Jaakko Peltonen, Ari Hottinen, Mik...
IIE
2007
63views more  IIE 2007»
14 years 9 months ago
Investigation of Q-Learning in the Context of a Virtual Learning Environment
We investigate the possibility to apply a known machine learning algorithm of Q-learning in the domain of a Virtual Learning Environment (VLE). It is important in this problem doma...
Dalia Baziukaite
JMLR
2010
189views more  JMLR 2010»
14 years 4 months ago
Adaptive Step-size Policy Gradients with Average Reward Metric
In this paper, we propose a novel adaptive step-size approach for policy gradient reinforcement learning. A new metric is defined for policy gradients that measures the effect of ...
Takamitsu Matsubara, Tetsuro Morimura, Jun Morimot...