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» Coarticulation in Markov Decision Processes
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IJCAI
2003
13 years 10 months ago
Multiple-Goal Reinforcement Learning with Modular Sarsa(0)
We present a new algorithm, GM-Sarsa(0), for finding approximate solutions to multiple-goal reinforcement learning problems that are modeled as composite Markov decision processe...
Nathan Sprague, Dana H. Ballard
AUTOMATICA
2008
74views more  AUTOMATICA 2008»
13 years 9 months ago
Policy iteration based feedback control
It is well known that stochastic control systems can be viewed as Markov decision processes (MDPs) with continuous state spaces. In this paper, we propose to apply the policy iter...
Kan-Jian Zhang, Yan-Kai Xu, Xi Chen, Xi-Ren Cao
CORR
2006
Springer
100views Education» more  CORR 2006»
13 years 9 months ago
Capacity of Cooperative Fusion in the Presence of Byzantine Sensors
Abstract-- The problem of cooperative fusion in the presence of both Byzantine sensors and misinformed sensors is considered. An information theoretic formulation is used to charac...
Oliver Kosut, Lang Tong
CORR
2000
Springer
129views Education» more  CORR 2000»
13 years 8 months ago
Prosody-Based Automatic Segmentation of Speech into Sentences and Topics
A crucial step in processing speech audio data for information extraction, topic detection, or browsing/playback is to segment the input into sentence and topic units. Speech segm...
Elizabeth Shriberg, Andreas Stolcke, Dilek Z. Hakk...
ISAAC
2010
Springer
243views Algorithms» more  ISAAC 2010»
13 years 7 months ago
Lower Bounds for Howard's Algorithm for Finding Minimum Mean-Cost Cycles
Howard's policy iteration algorithm is one of the most widely used algorithms for finding optimal policies for controlling Markov Decision Processes (MDPs). When applied to we...
Thomas Dueholm Hansen, Uri Zwick