Guiding Combinatorial Optimization with UCT

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Learning Combinatorial Optimization Algorithms over Graphs (2017, methodology): “This thread includes machine learning methods for branching [26, 22], tree node selection [16, 34], and heuristic selection [35, 23].”
On learning and branching: a survey (2017, background): “In Sabharwal et al. (2012), the exploited framework is that of reinforcement learning (see Sect.”
A Survey of Monte Carlo Tree Search Methods (2012, methodology): “Mixed Integer Programming In the study performed by Sabharwal and Samulowitz [179], UCT is applied to guide Mixed Integer Programming (MIP), comparing the performance of the UCT based node selection with that of CPLEX, a traditional MIP solver, and best-first, breadth-first and depth-first strategie”
Model-Based Planning with Discrete and Continuous Actions (2017, methodology): “This method can be shown to achieve optimal performance given a perfect environment model and enough simulated rollouts, and has been successfully used in contexts such as games (Silver et al., 2016), combinatorial optimization (Sabharwal et al., 2012) and scheduling (Cazenave et al., 2009).”