Typical approaches to relational MDPs consider only discrete variables or else discretize the continuous variables prior to inference or learning. In contrast, we consider hybrid relational MDPs, which are represented as probabilistic programs and specify the probability density function of the continuous variables. Our key contribution is that we introduce a technique for learning their structure (and parameters) from data. The learned models contain rich relational descriptions as well as mathematical equations. We demonstrate the utility of our approach by learning a model that accurately predicts the effects of robot-arm actions. The learned model is then used for planning tasks.
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