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Learning Classical Planning Strategies with Policy Gradient
[article]
2019
arXiv
pre-print
A common paradigm in classical planning is heuristic forward search. Forward search planners often rely on simple best-first search which remains fixed throughout the search process. In this paper, we introduce a novel search framework capable of alternating between several forward search approaches while solving a particular planning problem. Selection of the approach is performed using a trainable stochastic policy, mapping the state of the search to a probability distribution over the
arXiv:1810.09923v2
fatcat:4pcd7r373rfdfj7j7w544ezwza