We present an any-time concurrent probabilistic temporal planner that includes continuous and discrete uncertainties and metric functions. Our approach is a direct policy search t...
System-level design presents special simulation modeling challenges. System-level models address the architectural and functional performance of complex systems. Systems are decom...
Gunar Schorcht, Ian A. Troxel, Keyvan Farhangian, ...
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learning in continuous state spaces and discrete time. We demonstrate how the GP mod...
We present a stochastic approximation method to compute bid prices in network revenue management problems. The key idea is to visualize the total expected revenue as a function of...
Efficient representations and solutions for large decision problems with continuous and discrete variables are among the most important challenges faced by the designers of automa...
Branislav Kveton, Milos Hauskrecht, Carlos Guestri...