Efficient representations and solutions for large decision problems with
continuous and discrete variables are among the most important challenges faced
by the designers of automated decision support systems. In this paper, we
describe a novel hybrid factored Markov decision process (MDP) model that
allows for a compact representation of these problems, and a new hybrid
approximate linear programming (HALP) framework that permits their efficient
solutions. The central idea of HALP is to approximate the optimal value
function by a linear combination of basis functions and optimize its weights by
linear programming. We analyze both theoretical and computational aspects of
this approach, and demonstrate its scale-up potential on several hybrid
optimization problems.

本文介绍了一种可对连续和离散变量的大决策问题进行紧凑表示的混合分解 Markov 决策过程（MDP）模型和一种新的混合近似线性规划（HALP）框架，HALP 的核心思想是通过一组基函数的线性组合来近似最优价值函数，并通过线性规划来优化其权重， 并证明其在多种混合优化问题中的可扩展潜力。