Solving Factored MDPs with Continuous and Discrete Variables

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 cannot adequately address these problems. We present the first framework that can exploit problem structure for modeling and solving hybrid problems efficiently. We formulate these problems as hybrid Markov decision processes (MDPs with continuous and discrete state and action variables), which we assume can be represented in a factored way using a hybrid dynamic Bayesian network (hybrid DBN). This formulation also allows us to apply our methods to collaborative multiagent settings. We present a new linear program approximation method that exploits the structure of the hybrid MDP and lets us compute approximate value functions more efficiently. In particular, we describe a new factored discretization of continuous variables that avoids the exponential blow-up of traditional approaches. We provide theoretical ...

• Type: Conference
• Year: 2004
• Where: UAI
• Authors: Carlos Guestrin, Milos Hauskrecht, Branislav Kveton
• Keywords: Continuous Variables, Hybrid Markov Decision, Hybrid Problems, UAI 2004, UAI 2008