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» Online Planning Algorithms for POMDPs
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JAIR
2008
130views more  JAIR 2008»
13 years 5 months ago
Online Planning Algorithms for POMDPs
Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP i...
Stéphane Ross, Joelle Pineau, Sébast...
NIPS
2007
13 years 7 months ago
Theoretical Analysis of Heuristic Search Methods for Online POMDPs
Planning in partially observable environments remains a challenging problem, despite significant recent advances in offline approximation techniques. A few online methods have a...
Stéphane Ross, Joelle Pineau, Brahim Chaib-...
AAAI
2010
13 years 7 months ago
PUMA: Planning Under Uncertainty with Macro-Actions
Planning in large, partially observable domains is challenging, especially when a long-horizon lookahead is necessary to obtain a good policy. Traditional POMDP planners that plan...
Ruijie He, Emma Brunskill, Nicholas Roy
PRICAI
2000
Springer
13 years 9 months ago
A POMDP Approximation Algorithm That Anticipates the Need to Observe
This paper introduces the even-odd POMDP, an approximation to POMDPs in which the world is assumed to be fully observable every other time step. The even-odd POMDP can be converte...
Valentina Bayer Zubek, Thomas G. Dietterich
ICRA
2008
IEEE
173views Robotics» more  ICRA 2008»
14 years 2 days ago
Bayesian reinforcement learning in continuous POMDPs with application to robot navigation
— We consider the problem of optimal control in continuous and partially observable environments when the parameters of the model are not known exactly. Partially Observable Mark...
Stéphane Ross, Brahim Chaib-draa, Joelle Pi...