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» Mean-Variance Optimization in Markov Decision Processes
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UAI
2008
13 years 9 months ago
Partitioned Linear Programming Approximations for MDPs
Approximate linear programming (ALP) is an efficient approach to solving large factored Markov decision processes (MDPs). The main idea of the method is to approximate the optimal...
Branislav Kveton, Milos Hauskrecht
FOCS
2007
IEEE
14 years 2 months ago
Approximation Algorithms for Partial-Information Based Stochastic Control with Markovian Rewards
We consider a variant of the classic multi-armed bandit problem (MAB), which we call FEEDBACK MAB, where the reward obtained by playing each of n independent arms varies according...
Sudipto Guha, Kamesh Munagala
NIPS
2007
13 years 9 months ago
What makes some POMDP problems easy to approximate?
Point-based algorithms have been surprisingly successful in computing approximately optimal solutions for partially observable Markov decision processes (POMDPs) in high dimension...
David Hsu, Wee Sun Lee, Nan Rong
IJRR
2008
101views more  IJRR 2008»
13 years 7 months ago
Motion Planning Under Uncertainty for Image-guided Medical Needle Steering
We develop a new motion planning algorithm for a variant of a Dubins car with binary left/right steering and apply it to steerable needles, a new class of flexible beveltip medica...
Ron Alterovitz, Michael S. Branicky, Kenneth Y. Go...
AIPS
2000
13 years 9 months ago
On-line Scheduling via Sampling
1 We consider the problem of scheduling an unknown sequence of tasks for a single server as the tasks arrive with the goal off maximizing the total weighted value of the tasks serv...
Hyeong Soo Chang, Robert Givan, Edwin K. P. Chong