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GLOBECOM
2007
IEEE
10 years 8 months ago
Bursty Traffic in Energy-Constrained Opportunistic Spectrum Access
We design opportunistic spectrum access strategies for improving spectrum efficiency. In each slot, a secondary user chooses a subset of channels to sense and decides whether to ac...
Yunxia Chen, Qing Zhao, Ananthram Swami
COLT
2000
Springer
10 years 8 months ago
Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning
We model reinforcement learning as the problem of learning to control a Partially Observable Markov Decision Process (  ¢¡¤£¦¥§  ), and focus on gradient ascent approache...
Peter L. Bartlett, Jonathan Baxter
ECML
2005
Springer
10 years 9 months ago
Active Learning in Partially Observable Markov Decision Processes
This paper examines the problem of finding an optimal policy for a Partially Observable Markov Decision Process (POMDP) when the model is not known or is only poorly specified. W...
Robin Jaulmes, Joelle Pineau, Doina Precup
IAT
2005
IEEE
10 years 9 months ago
Decomposing Large-Scale POMDP Via Belief State Analysis
Partially observable Markov decision process (POMDP) is commonly used to model a stochastic environment with unobservable states for supporting optimal decision making. Computing ...
Xin Li, William K. Cheung, Jiming Liu
ICRA
2007
IEEE
134views Robotics» more  ICRA 2007»
10 years 10 months ago
Grasping POMDPs
Abstract— We provide a method for planning under uncertainty for robotic manipulation by partitioning the configuration space into a set of regions that are closed under complia...
Kaijen Hsiao, Leslie Pack Kaelbling, Tomás ...
ICRA
2007
IEEE
154views Robotics» more  ICRA 2007»
10 years 10 months ago
Oracular Partially Observable Markov Decision Processes: A Very Special Case
— We introduce the Oracular Partially Observable Markov Decision Process (OPOMDP), a type of POMDP in which the world produces no observations; instead there is an “oracle,” ...
Nicholas Armstrong-Crews, Manuela M. Veloso
ICRA
2007
IEEE
126views Robotics» more  ICRA 2007»
10 years 10 months ago
A formal framework for robot learning and control under model uncertainty
— While the Partially Observable Markov Decision Process (POMDP) provides a formal framework for the problem of robot control under uncertainty, it typically assumes a known and ...
Robin Jaulmes, Joelle Pineau, Doina Precup
ICC
2007
IEEE
121views Communications» more  ICC 2007»
10 years 10 months ago
Structure and Optimality of Myopic Sensing for Opportunistic Spectrum Access
We consider opportunistic spectrum access for secondary users over multiple channels whose occupancy by primary users is modeled as discrete-time Markov processes. Due to hardware...
Qing Zhao, Bhaskar Krishnamachari
DATE
2007
IEEE
133views Hardware» more  DATE 2007»
10 years 10 months ago
Stochastic modeling and optimization for robust power management in a partially observable system
As the hardware and software complexity grows, it is unlikely for the power management hardware/software to have a full observation of the entire system status. In this paper, we ...
Qinru Qiu, Ying Tan, Qing Wu
ICC
2008
IEEE
169views Communications» more  ICC 2008»
10 years 10 months ago
Optimality of Myopic Sensing in Multi-Channel Opportunistic Access
—We consider opportunistic communications over multiple channels where the state (“good” or “bad”) of each channel evolves as independent and identically distributed Mark...
Tara Javidi, Bhaskar Krishnamachari, Qing Zhao, Mi...
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