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IJCAI
2003
15 years 2 months ago
Taming Decentralized POMDPs: Towards Efficient Policy Computation for Multiagent Settings
The problem of deriving joint policies for a group of agents that maximize some joint reward function can be modeled as a decentralized partially observable Markov decision proces...
Ranjit Nair, Milind Tambe, Makoto Yokoo, David V. ...
TSP
2008
107views more  TSP 2008»
15 years 1 months ago
Opportunistic Spectrum Access via Periodic Channel Sensing
The problem of opportunistic access of parallel channels occupied by primary users is considered. Under a continuous-time Markov chain modeling of the channel occupancy by the prim...
Qing Zhao, Stefan Geirhofer, Lang Tong, Brian M. S...
IROS
2009
IEEE
206views Robotics» more  IROS 2009»
15 years 8 months ago
Bayesian reinforcement learning in continuous POMDPs with gaussian processes
— Partially Observable Markov Decision Processes (POMDPs) provide a rich mathematical model to handle realworld sequential decision processes but require a known model to be solv...
Patrick Dallaire, Camille Besse, Stéphane R...
AOR
2010
14 years 10 months ago
Speeding up Stochastic Dynamic Programming with Zero-Delay Convolution
We show how a technique from signal processing known as zero-delay convolution can be used to develop more efficient dynamic programming algorithms for a broad class of stochastic...
Brian C. Dean
AAAI
1997
15 years 2 months ago
Structured Solution Methods for Non-Markovian Decision Processes
Markov Decision Processes (MDPs), currently a popular method for modeling and solving decision theoretic planning problems, are limited by the Markovian assumption: rewards and dy...
Fahiem Bacchus, Craig Boutilier, Adam J. Grove