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UAI
2008
13 years 11 months ago
Hierarchical POMDP Controller Optimization by Likelihood Maximization
Planning can often be simplified by decomposing the task into smaller tasks arranged hierarchically. Charlin et al. [4] recently showed that the hierarchy discovery problem can be...
Marc Toussaint, Laurent Charlin, Pascal Poupart
AIPS
2003
13 years 11 months ago
Synthesis of Hierarchical Finite-State Controllers for POMDPs
We develop a hierarchical approach to planning for partially observable Markov decision processes (POMDPs) in which a policy is represented as a hierarchical finite-state control...
Eric A. Hansen, Rong Zhou
DATE
2008
IEEE
136views Hardware» more  DATE 2008»
14 years 4 months ago
A Framework of Stochastic Power Management Using Hidden Markov Model
- The effectiveness of stochastic power management relies on the accurate system and workload model and effective policy optimization. Workload modeling is a machine learning proce...
Ying Tan, Qinru Qiu
IROS
2009
IEEE
206views Robotics» more  IROS 2009»
14 years 4 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...
ICRA
2008
IEEE
173views Robotics» more  ICRA 2008»
14 years 4 months 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...