Although many real-world stochastic planning problems are more naturally formulated by hybrid models with both discrete and continuous variables, current state-of-the-art methods ...
Carlos Guestrin, Milos Hauskrecht, Branislav Kveto...
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...
Markov decision processes (MDPs) with discrete and continuous state and action components can be solved efficiently by hybrid approximate linear programming (HALP). The main idea ...
Approximate linear programming (ALP) offers a promising framework for solving large factored Markov decision processes (MDPs) with both discrete and continuous states. Successful ...
Approximate linear programming (ALP) has emerged recently as one of the most promising methods for solving complex factored MDPs with finite state spaces. In this work we show th...