We consider sensor scheduling as the optimal observability problem for partially observable Markov decision processes (POMDP). This model fits to the cases where a Markov process ...
We address the problem of optimally controlling stochastic environments that are partially observable. The standard method for tackling such problems is to define and solve a Part...
—Controlled mobile helper nodes called data ferries have recently been proposed to bridge communications between disconnected nodes in a delay-tolerant manner. While existing wor...
Chi Harold Liu, Ting He, Kang-won Lee, Kin K. Leun...
We consider the problem of estimating the policy gradient in Partially Observable Markov Decision Processes (POMDPs) with a special class of policies that are based on Predictive ...
Partially observable Markov decision processes (POMDPs) provide a principled, general framework for robot motion planning in uncertain and dynamic environments. They have been app...
Sylvie C. W. Ong, Shao Wei Png, David Hsu, Wee Sun...