Planning in Discrete and Continuous Markov Decision Processes by Probabilistic Programming

23 citations recorded by the source corpus.

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Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds (2017, methodology): “To put this to practice, we have enabled data association using expressions such as [Nitti et al., 2015b; 2015a]:”
Lifted Stochastic Planning, Belief Propagation and Marginal MAP (2018, background): “…2011; Neumann 2011; Kober and Peters 2011; Sabbadin, Peyrard, and Forsell 2012; Liu and Ihler 2012; Cheng et al. 2013; Kiselev and Poupart 2014; Nitti, Belle, and Raedt 2015; van de Meent et al. 2016; Lee, Mari-nescau, and Dechter 2016; Nguyen, Kumar, and Lau 2017) with applications in…”
Black-Box Policy Search with Probabilistic Programs (2015, methodology): “In contrast to languages that target a single domainspecific algorithm [Andre and Russell, 2002, Srivastava et al., 2014, Nitti et al., 2015], our formulation emphasizes the use of general-purpose techniques for Bayesian inference, in which learning is used for inference amortization.”
Sample-based abstraction for hybrid relational MDPs (2015, methodology): “In this section we empirically evaluates HybRel with respect to HYPE, i.e., the effect of episode abstraction on performance.”