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ICRA
2008
IEEE
173views Robotics» more  ICRA 2008»
15 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...
92
Voted
NIPS
2003
14 years 11 months ago
A Nonlinear Predictive State Representation
Predictive state representations (PSRs) use predictions of a set of tests to represent the state of controlled dynamical systems. One reason why this representation is exciting as...
Matthew R. Rudary, Satinder P. Singh
EOR
2006
66views more  EOR 2006»
14 years 9 months ago
Performance prediction of an unmanned airborne vehicle multi-agent system
Consider unmanned airborne vehicle (UAV) control agents in a dynamic multi-agent system. The agents must have a set of goals such as destination airport and intermediate positions...
Zhaotong Lian, Abhijit Deshmukh
102
Voted
GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
14 years 7 months ago
Uncertainty handling CMA-ES for reinforcement learning
The covariance matrix adaptation evolution strategy (CMAES) has proven to be a powerful method for reinforcement learning (RL). Recently, the CMA-ES has been augmented with an ada...
Verena Heidrich-Meisner, Christian Igel
87
Voted
CORR
2010
Springer
103views Education» more  CORR 2010»
14 years 8 months ago
Structural Solutions to Dynamic Scheduling for Multimedia Transmission in Unknown Wireless Environments
In this paper, we propose a systematic solution to the problem of scheduling delay-sensitive media data for transmission over time-varying wireless channels. We first formulate th...
Fangwen Fu, Mihaela van der Schaar